Browse Topic: Active safety systems

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IMPLEMENTATION OF THE 4D/RCS ARCHITECTURE WITHIN THE SOUTHWEST SAFE TRANSPORT INITIATIVE2024-01-3086To be published on 11/15/2027
ABSTRACT Over time, the National Institute of Standards and Technology (NIST) has refined the 4Dimension / Real-time Control System (4D/RCS) architecture for use in Unmanned Ground Vehicles (UGVs). This architecture, when applied to a fully autonomous vehicle designed for missions in urban environments, can greatly assist in the process of saving time and lives by creating a more intelligent vehicle that acts in a safer and more efficient manner. Southwest Research Institute (SwRI®) has undertaken the Southwest Safe Transport Initiative (SSTI) aimed at investigating the development and commercialization of vehicle autonomy as well as vehicle-based telemetry systems to improve active safety systems and autonomy. This paper will discuss the implementation of the 4D/RCS architecture to the SSTI autonomous vehicle, a 2006 Ford Explorer.
McWilliams, George, Brown, Michael
Guidelines for the Integration of Electronic Engine Control Systems for Transport Category (Part 25) and General Aviation (Part 23) AircraftAIR5924B (Current)11/26/2025
This SAE Aerospace Information Report (AIR) provides methodologies and approaches that have been used to install and integrate full-authority-digital-engine-control (FADEC) systems on transport category aircraft. Although most of the information provided is based on turbofan engines installed on large commercial transports, many of the issues raised are equally applicable to corporate, general aviation, regional and commuter aircraft, and to military installations, particularly when commercial aircraft are employed by military users. The word “engine” is used to designate the aircraft propulsion system. The engine station designations used in this report are shown in Figure 1. Most of the material concerns an Electronic Engine Control (EEC) with its associated software, and its functional integration with the aircraft. However, the report also addresses the physical environment associated with the EEC and its associated wiring and sensors. Since most of today’s transport category engines use dual-channel full-authority digital engine control (FADEC) systems, this is the configuration which is addressed. A typical FADEC system configuration is shown in Figure 2.
E-36 Electronic Engine Controls Committee
Detect and Avoid function for UAVs using ACAS Xu for Non-Cooperative TargetsF-0081-2025-02745/20/2025
Advanced Air Mobility (AAM) is an innovative concept that aims to revolutionize air transportation through electric and unmanned aircraft, enabling applications such as urban air taxis and medical transport. However, one of the key challenges to its widespread adoption is ensuring safety, particularly in collision avoidance. This study focuses on the development of a perception and guidance system for avoiding collisions with non-cooperative targets, which do not share their position or trajectory. To achieve this, a Frequency-Modulated Continuous Wave (FMCW) radar and an InfraRed(IR) camera are used. Compared to traditional pulsed or panel radars, FMCW radars offer higher resolution, better detection of small and slow-moving objects, and improved performance in cluttered environments. The IR camera enhances situational awareness by providing visual confirmation and additional tracking capability, making this sensor fusion approach particularly suitable for AAM applications. Our collision avoidance system follows ACAS Xu standards, which provide autonomous conflict detection and resolution for unmanned aerial vehicles. The maneuver selection process is based on precomputed lookup tables generated through a Markov Decision Process (MDP), optimizing responses based on risk and energy consumption. The entire system is tested in a simulation environment using Ansys AVxcelerate, a physics-based simulator capable of generating realistic sensor data. This approach allows for comprehensive testing of detection, tracking, and maneuver execution in a highly realistic scenario, ensuring the effectiveness of the proposed solution before real-world deployment.
Brivio, Riccardo, Crippa, Anna, Baiguera, Matteo, Portanti, Samuele, Bertolo, Mattia
EVOLUTION OF 360 DEGREE LOCAL SITUATIONAL AWARENESS (LSA) TO AID THE WARFIGHTER2024-01-345111/15/2024
ABSTRACT Northrop Grumman has developed a software and hardware solution to provide enhanced 360 degree local situational awareness (LSA) to enable the warfighter with an overmatch capability on today’s modern battlefield. The architecture exploits technological gains in cameras, video processing, and video compression. The approach allows rapid comprehension of local and remote situational views presented with operational relevance for a ground combat platform or tactical wheeled platform crew. The 360 Degree LSA approach provides direct visualization of relative positioning of targets, threats, and lines of fire; and additionally offers common situational understanding / operational picture from the dismounted soldier to higher echelon commands. The approach provides prioritized information through LSA software to provide an enhanced view to the warfighter whereas the squad leader becomes an integral part of the crew with a view of the common operating picture (mounted) and additional sensors on tablet or handheld device (dismounted via wireless). The approach uses a platform agnostic form factor with components that can be selected and applied to legacy or new platforms based on their size, weight, power, and mission constraints.
Viscovich, Christopher, Geoghegan, Susan, Worthy, David
Test:15/2SAE-PP-078102/15/2023
Test:15/2
Mutagaana, Festo
test 11/01/2022 10:40 amauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testSAE-PP-0778011/1/2022
test 11/01/2022 10:40 am
Mutagaana, Festo
Test Preprint 20220628 By Madhukar DongariSAE-PP-025996/28/2022
Test Preprint 20220628 By Madhukar Dongari
Mutagaana, Festo
Blind Spot Monitoring System (BSMS): Operating Characteristics and User InterfaceJ2802_202110 (Current)10/12/2021
This document specifies the minimum recommendations for Blind Spot Monitoring System (BSMS) operational characteristics and elements of the user interface. A visual BSMS indicator is recommended. BSMS detects and conveys to the driver via a visual indicator the presence of a target (e.g., a vehicle), adjacent to the subject vehicle in the “traditional” Adjacent Blind Spot Zone (ABSZ). The BSMS is not intended to replace the need for interior and exterior rear-view mirrors or to reduce mirror size. BSMS is only intended as a supplement to these mirrors and will not take any automatic vehicle control action to prevent possible collisions. While the BSMS will assist drivers in detecting the presence of vehicles in their ABSZ, the absence of a visual indicator will not guarantee that the driver can safely make a lane change maneuver (e.g., vehicles may be approaching rapidly outside the ABSZ area). This document applies to original equipment and aftermarket BSMS systems for passenger vehicles. This document does not apply to installing a BSMS on either motorcycles or commercial vehicles. Finally, this document does not address Lane Change Warning systems, which monitor areas substantially farther back than the side blind spot areas monitored by the BSMS (See ISO FDIS 17387).
Advanced Driver Assistance Systems (ADAS) Committee
Taxonomy and Definition of Safety Principles for Automated Driving System (ADS)J3206_202107 (Current)7/7/2021
This SAE Information Report classifies and defines a harmonized set of safety principles intended to be considered by ADS and ADS-equipped vehicle development stakeholders. The set of safety principles herein is based on the collection and analysis of existing information from multiple entities, reflecting the content and spirit of their efforts, including: SAE ITC AVSC Best Practices CAMP Automated Vehicle Research for Enhanced Safety - Final Report RAND Report - Measuring Automated Vehicle Safety: Forging a Framework U.S. DOT: Automated Driving Systems 2.0 - A Vision for Safety Safety First for Automated Driving (SaFAD) UNECE WP29 amendment proposal UNECE/TRANS/WP.29/GRVA/2019/13 On a Formal Model of Safe and Scalable Self-Driving Cars (Intel RSS model) SAE J3018 This SAE Information Report provides guidance for the consideration and application of the safety principles for the development and deployment of ADS and ADS-equipped vehicles. This SAE Information Report is not intended to encompass all aspects of system-level safety for an ADS-equipped vehicle, including communication with other traffic participants. Addressing all identified safety principles is intended to support, but not fully ensure, comprehensive system-level safety. As an SAE Information Report, this document is non-normative, imposes no requirements, and does not address: Requirements for methodology, metrics, and/or acceptance thresholds. Ethics-related safety principles, or any link between the safety principles defined in this document and ethical studies/frameworks. Conformance with safety principles for purposes of liability and/or fault assignment. As ADS technology and deployment are expanded in the future, this document may be reconsidered for future revision including normative requirements.
On-Road Automated Driving (ORAD) committee
Impact of Fog Particles on 1.55 μm Automotive LiDAR Sensor Performance: An Experimental Study in an Enclosed Chamber2021-01-00814/6/2021
To achieve full automation in self-driving vehicles, environmental perception sensing accuracy is critically important. However, ambient particles in adverse weather like foggy, rainy, or snowy conditions can significantly scatter the incident laser beam, and therefore contaminate the intensity and accuracy of light detection and ranging (LiDAR) sensors. Especially compared to the rapidity of technology development in self-driving vehicles, there is a significant lack of documented research on LiDAR systems with wavelength longer than 1 μm for application in Advanced Driver-Assistance Systems. In this work, experimental studies were performed with a state-of-the-art 1.55 μm wavelength automotive-grade LiDAR system in a controlled laboratory fog chamber. The goal of the research is to correlate laser attenuation and the optical properties of fog particles. In this work, a thorough multistep procedure for LiDAR data analysis is presented including spatial averaging of the object measurement and characterizing the temperature effect on a LiDAR intensity parameter. Fog particle density is measured by a commercial visibility sensor instrument. Assuming a constant extinction coefficient and backscatter coefficient, a simple analytical model is derived that correlates LiDAR reflectance and extinction coefficient measured by visibility sensor. Results show that the correlation coefficient between LiDAR and visibility sensor data is 0.98 and the R-squared value of linear fitting is 0.96. By comparing the LiDAR original signal and the model, the Root-Mean-Squared Deviation is 0.007, meaning the model performs very well for predicting LiDAR reflectance in the controlled environment. Furthermore, although the returned signal strength is attenuated, the LiDAR can measure the target with a visibility range lower than six meters.
Zhan, Lu, Northrop, William F.
A Semantic Slam System Based on Visual-Inertial Information and around View Images for Underground Parking Lot2021-01-00784/6/2021
As one of the most challenging driving tasks, parking is a common but particularly troublesome problem in large cities. Recently, an excellent solution-automated valet parking (AVP) has become a hot research topic, which allows the driver to leave the vehicle in a drop-off area, while the vehicle driving into the parking slot by itself. For AVP, the precise localization is an indispensable module. However, the global positioning system (GPS) cannot be used in the underground parking lot and the localization method based on lidar is too expensive. In response to solve this problem, we propose a simultaneous localization and mapping system with the semantic information of parking slots (PS-SLAM), which is based on visual-inertial and around view images. First, the calibration of multi-sensors is conducted to obtain their intrinsic and extrinsic parameters. In this way, the around view image and transformation matrices between sensors can be acquired. Then, the ORB-SLAM3 based on visual-inertial information is used to acquire the pose of the vehicle and sparse point cloud map. Next, the parking slot in the around view image is detected by the deep convolutional neural network (DCNN) model called VPS-Net. Finally, a parking-slot association method is devised to associate the detected parking slots with the point cloud map to generate a semantic map. The field experiments are conducted using a wire control chassis with 4 fisheye cameras, an inertial measurement unit (IMU), and a monocular camera. The results show that the proposed visual semantic SLAM system not only can achieve centimeter-level localization in the indoor parking lot but also generate a semantic map with parking slots.
LI, Wei, Li, Chaohui, Xiao, Dongjie, Zhou, Dong, Wang, Tao, Cao, Libo
Object Segmentation and Augmented Visualization Based on Panoramic Image Segmentation2021-01-00894/6/2021
Panoramic images can provide critical information for Advanced Driving Assistance Systems (ADAS), such as parking spaces and surrounding vehicles. However, the vehicle in the bird's-eye view image is severely distorted and incomplete, and the visual information becomes very blurred in some illumination insufficient environments. If the driver cannot see the surrounding environment information, the risk of collision will increase, especially during parking. To better percept the local environment with the help of panoramic images, we use panoramic image segmentation results to construct a virtual surround view monitoring system to provide drivers with clearer perception information. Firstly, a lightweight segmentation network is redesigned based on SegNet, which will improve the accuracy of the segmentation without increasing the model’s inference time. Secondly, we build an augment visualization around view monitor (AV-AVM) system with regards to the segmentation results. All necessary segmentation results will be presented as augmented visualization in AV-AVM systems, such as parking slots and road markings. Compared with the traditional panoramic system, the virtual panoramic surround view system we designed can provide the driver with more intuitive environmental perception information and can be further used to construct an automatic parking map.
Liao, Jiacai, Cao, Libo, Gong, Yipeng, Zhao, Junjie, Chen, Zhen, Chen, Kai
Research on Photobiological Safety of Automotive Active Infrared Detection System2021-01-00724/6/2021
The automotive active infrared detection system is usually applied to the night driver assistance system or the diver attention monitoring system. However, the infrared light emitted by the active infrared detection system can cause damage to retina, cornea and eye crystals. This paper has studied the photobiological safety of the infrared light source used in the automotive active infrared detection system. Although it has been already have the general requirements of photobiological safety in international standards, there is not any requirements for automotive active infrared detection system. The range of the active infrared detection system depends on the radiation intensity of the infrared light source, but too much radiation intensity will cause harm to retina, cornea and eye lens when the infrared light source is too close to eyes. Based on the international standards, this paper has analyzed the retinal thermal hazard and the harm to skin and eyes caused by infrared radiation, and it has calculated the theoretical radiation intensity threshold under different risk groups and safety distances. The threshold of radiation intensity of the automotive active infrared detection system have been proposed, which could provide references for the formulation of Chinese national standard. What’s more, the experimental verification has been conducted by using the driver attention monitoring system, and the conclusion indicates that the threshold proposed in this paper is reasonable.
Hu, Yue, Zhu, Tong, Li, Qian, Yang, Xiong
Multi-Sensor Fusion in Slow Lanes for Lane Keep Assist System2021-01-00844/6/2021
Implementing Advanced Driver Assistance Systems (ADAS) features that are available in all road scenarios and weather conditions is a big challenge for automotive companies and considered key enablers to achieve autonomous Level 4 (L4) vehicles. One important feature is the Lane Keep Assist System (LKAS). Most LKAS systems are based on lane line detection cameras and lane coefficient estimations by the camera is the key point for LKAS where the camera recognizes the lane lines using edge detection. But when the lane markers are not available due to high traffic and slow driving on the roads, another source of data for the lane lines needs to be available for the LKAS. In this paper a multi-sensor fusion approach based on camera, Lidar, and GPS is used to allow the vehicle to maintain its lateral location within the lane. The lateral distances of the lane lines are measured by LiDAR detection of the markers based on the intensity and fused with lane line information from the HD Map after transforming the sensors to the same reference. This approach was tested on FEV’s Smart Vehicle Demonstrator and the test results show the vehicle was able to maintain the lane.
Alrousan, Qusay, Matta, Sherif, Tasky, Tom
Correlation between Sensor Performance, Autonomy Performance and Fuel-Efficiency in Semi-Truck Platoons2021-01-00644/6/2021
Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel. This is especially an issue if deceleration is applied in response to a bad measurement. In this study, a faulty radar was shown to cause a greater than 5% increase in fuel consumption. The mechanism of this fuel consumption increase is investigated and applied to other types of sensor failures to indicate their potential effects on fuel economy. This analysis indicates that poor GPS signals over short time can be largely filtered out, with no real gain or loss of fuel economy. V2V communications were intentionally limited by causing interference, which resulted in dropped communication packets over a small physical area, but not an appreciable impact on fuel economy.
Adam, Cristian, Lakshmanan, Sridhar, Richardson, Paul, Stegner, Evan, Ward, Jacob, Hoffman, Mark, Bevly, David M.
Future Automotive Embedded Systems Enabled by Efficient Model-Based Software Development2021-01-01294/6/2021
This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS). However, the increased system complexity challenges the efficiency of system verification during product development. The industry cannot afford delays in design cycles and efficient utilization of R&D resources is an important success factor. Model-based controls and software development with automatic code generation is an important dimension to resolve this challenge. It enables efficient algorithm development and verification and, thereby, supports to achieve ISO26262 compliance during product development. This is explained in this paper along three perspectives: Firstly, a use case overview explains the drivers for more advanced algorithms and, therefore, more high-performance computing resources. Secondly, a tool flow is proposed, which provides an efficient model-based controls and software development environment for next generation heterogeneous embedded systems. And lastly, this proposal is tested against automakers requirements for software and function development. Combining these perspectives sheds light on future automotive embedded software and systems, which experience an increasing relevance as demonstrated by recent automakers decisions to increasingly take ownership of software development.
Schaefer, Juergen, Christlbauer, Herbert, Schreiber, Alexander, Reith, Graham, Jonker, Mischa, Potman, Jordy, Dannebaum, Udo, Eissfeldt, Tjark
AVSC Best Practice for Metrics and Methods for Assessing Safety Performance of Automated Driving Systems (ADS)AVSC000062021033/25/2021
This AVSC Best Practice for Metrics and Methods for Assessing Safety Performance of Automated Driving Systems (ADS) (AVSC00006202103) recommends a set of metrics that may be used to assess ADS safety performance of the dynamic driving task (DDT). These metrics and methods are principally designed to provide evidence of safety performance for a manufacturer’s decision to deploy (and monitor) fleet-operated/managed SAE level 4 and 5 ADS-dedicated vehicles (ride-hailing or product delivery). This document lays out a performance-based, technology-neutral approach for measuring and analyzing safety performance. It supports long-term, socially-important safety goals (like reducing crashes). ADS safety performance metrics in this document support system-level analyses, i.e. they are practical to implement for any system regardless of architecture. The best practice provides: Metrics to Support ADS Safety Recommended Safety Outcomes Recommended Predictive Safety Metrics Methods for Assessing DDT Performance Metrics The metrics and methods provided in this document are intended for use by the technical community (developers, manufacturers, testers, etc.) to aid in the safe development and deployment of ADS. They may also be useful to stakeholders who have interest in better understanding the safety posture of ADS deployments.
Automated Vehicle Safety Consortium
Letter from the Guest Editors12-04-01-00013/17/2021
Letter from the Guest Editors
Hamid, Umar Zakir Abdul, Sandblom, Fredrik, Habibovic, Azra, Li, Bin
1.1.201 - A Context Aware Automatic Image Enhancement Method Using Color TransferSAE-PP-002162/1/2021
Advanced Driver Assistance Systems (ADAS) have become an inevitable part of most of the modern cars. Their use is mandated by regulations in some cases; and in other cases where vehicle owners have become more safety conscious. Vision / camera based ADAS systems are widely in use today. However, it is to be noted that the performance of these systems is depends on the quality of the image/video captured by the camera. Low illumination is one of the most important factors which degrades image quality. In order to improve the system performance under low illumination, it is required to first enhance the input images/frames. In this paper, we propose an image enhancement algorithm that would automatically enhance images to a near ideal condition. This is accomplished by mapping features taken from images acquired under ideal illumination conditions on to the target low illumination images/frames. The proposed method consists of four steps a) Pre- processing b) a coarse level segmentation of the input image, c) searching for an appropriate images from the database and d) adaptive color transfer. Since our algorithm performs appropriate adaptive modification to various regions, the quality of resultant image is good even under low illumination condition. We have also done a quantitative evaluation using the entropy and contrast based measures. Results prove that our method performs much better both quantitatively and qualitatively when compared to the standard state of the art image enhancement tools that are widely used.
Mutagaana, Festo
Driver Drowsiness and Fatigue in the Safe Operation of Vehicles - Definition of Terms and ConceptsJ3198_202010 (Current)10/26/2020
This SAE Information Report provides definitions and discussions of key terms concerning driver drowsiness and fatigue, and basic information on measuring drowsiness and fatigue. It also includes information and concepts for driver drowsiness as they relate to the safe operation of a vehicle. The key driver drowsiness and fatigue causal factors include the following: (1) sleep quality and quantity, (2) time of day, (3) time awake, (4) time on task (modulated by characteristics of the driving task), (5) task-related fatigue (variations of arousal levels related to task underload and overload), and (6) combinations of these factors. Medical conditions, medication, alcohol, or drugs exacerbate drowsiness; however, the discussion in this report is limited to fatigue concepts. This report has two primary outputs: (1) definitions and discussions of key terms concerning driver drowsiness and fatigue, and (2) basic information on measuring drowsiness and fatigue and its effects on the safe operation of a vehicle. These include the physiological and cognitive effects of driver drowsiness and fatigue on driving safety. Examples of effect of driver drowsiness and fatigue on driving safety include those related to vehicle control, operator vigilance (sustained attention), reaction times (object and event detection and response), situational awareness, physiological indicators, subjective assessments, and combinations thereof. For definitions of driving performance measures, refer to SAE J2944. This report applies to all worldwide motor vehicle passenger cars and light trucks, as well as heavy trucks, buses, motorcycles, and mopeds. The intended users of the document are practitioners and researchers in the automotive industry, academia, and other organizations with interest in driver drowsiness and fatigue, driving and driver performance assessment, and road safety.
Driver Metrics, Performance, Behaviors and States Committee
Candidate Improvements to Dedicated Short Range Communications (DSRC) Message Set Dictionary [SAE J2735] Using Systems Engineering MethodsJ3067_202010 (Current)10/6/2020
This document is not a standard, it is a candidate for a standard being submitted to SAE for their consideration as a comment to SAE J2735. The term SAE J2735 SE candidate is used within this document to refer to this submission. This document specifies dialogs, messages, and the data frames and data elements that make up the messages specifically for use by applications intended to utilize the 5.9 GHz Dedicated Short Range Communications for Wireless Access in Vehicular Environments (DSRC/WAVE, referenced in this document simply as “DSRC"), communications systems. Although the scope of this Standard is focused on DSRC, these dialogs, messages, data frames and data elements have been designed, to the extent possible, to be of use for applications that may be deployed in conjunction with other wireless communications technologies. This standard therefore specifies the definitive message structure and provides sufficient background information to allow readers to properly interpret the message definitions from the point of view of an application developer implementing the messages according to the DSRC Standards.
V2X Communications Steering Committee
DATALINK GROUND SYSTEMS STANDARD AND INTERFACE SPECIFICATION (DGSS/IS)ARINC620-10 (Current)7/31/2020
ARINC Specification 620 defines the interfaces between the Datalink Service Provider (DSP) and the aircraft, other ground-based datalink services, and users. The datalink ground system standard definition supports traditional ACARS and AOA protocols, as well as Media Independent Aircraft Messaging (MIAM) as defined by ARINC Specification 841. MIAM messages can be much larger than ACARS messages (5 MB versus 3.3 kB per message). Supplement 10 improves Controller-Pilot Data Link Communications (CPDLC) by defining a “Deliver By (DB)” period that allows the DSP, that originated the message, to intercept and discard the message if it is not delivered by the specified time. Supplement 10 also adds a Media Advisory code for ACARS over IP (AoIP) indicating that an ACARS non Safety Services messages is being transferred over IP links. New Reason Codes are assigned for un-transmittable or undeliverable messages. ACARS Character set clarifications are also provided in Supplement 10.
Airlines Electronic Engineering Committee
Development of Design and Engineering Recommendations for In-Vehicle Alphanumeric MessagesJ2831_202007 (Current)7/29/2020
This Information Report provides recommendations for alphanumeric messages that are supplied to the vehicle by external (e.g., RDS, satellite radio) or internal (e.g., infotainment system) sources while the vehicle is in-motion. Information/design recommendations contained in this report apply to OEM (embedded) and aftermarket systems. Ergonomic issues with regard to display characteristics (e.g., viewing angle, brightness, contrast, font design, etc.) should review ISO 15008.
Driver Vehicle Interface (DVI) Committee
2021 Mustang Mach-E20AUTP08_017/1/2020
Ford's first dedicated electric platform reveals the challenges and opportunities in developing a state-of-the-art EV. The 2021 Ford Mustang Mach-E is an all-new vehicle. It's Ford's first dedicated battery electric vehicle (BEV) and it's built on an all-new electric vehicle (EV) platform. If that wasn't enough pressure on its development team, the Mach-E also leverages the Mustang nameplate traditionally reserved for iconic sports coupes and serves as a technology flagship debuting a host of new features to expand convenience and safety. One of the lead engineers has likened it to simultaneously engineering an EV, a cellphone and an SUV. “You're delivering the vehicle, that's old-school stuff, we created that at Ford. Now we've got to deliver the new-school stuff too,” explained Robert Iorio, vehicle engineering manager for the Mustang Mach-E.
Seredynski, Paul
Heavy Vehicle Event Data Recorder (HVEDR) - Data and TriggersJ2728-1_202006 (Current)6/9/2020
This document provides a list of data elements and event triggers for recording of event data relevant to crash investigations for heavy vehicles. The list of data elements includes recommended source(s) and formatting.
Truck and Bus Event Data Recorder Committee
On-Board System Requirements for V2V Safety CommunicationsJ2945/1_202004 (Current)4/30/2020
This standard specifies the system requirements for an on-board vehicle-to-vehicle (V2V) safety communications system for light vehicles1, including standards profiles, functional requirements, and performance requirements. The system is capable of transmitting and receiving the SAE J2735-defined basic safety message (BSM) [1] over a dedicated short range communications (DSRC) wireless communications link as defined in the Institute of Electrical and Electronics Engineers (IEEE) 1609 suite and IEEE 802.11 standards [2] to [6].
V2X Core Technical Committee
A Novel Prediction Algorithm for Heavy Vehicles System Rollover Risk Based on Failure Probability Analysis and SVM Empirical Model2020-01-07014/14/2020
The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability. Moreover, the paper establishes a classification model of heavy vehicles based on the support vector machine (SVM) and uses the Monte Carlo method to calculate the failure probability of rollover limit state of heavy vehicles. The fishhook, double lane change, and slalom maneuver tests of heavy vehicles are used to predict and validate the proposed algorithm in real-time. The simulation results show that the rollover prediction method based on failure probability is accurate and real-time, and can effectively improve the rollover prediction accuracy. Meanwhile, the proposed approach reduces the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus significantly improving the prediction accuracy of active safety performance of heavy vehicles.
Zhu, Tianjun, Yin, Xiaoxuan, Wang, Zhenfeng, Wang, Dong, Li, Fei, Wang, Xinyu, Ma, Wei, Wang, Zheng
Scenario Analysis and Control Comparison for a High Speed Autonomous Vehicle2020-01-07104/14/2020
This paper studies the simulation and control of an autonomous dragster. Four scenarios are provided that are critical to vehicle and driver safety in drag racing. Equations are then created to model the behavior during these safety scenarios. The use of a kinematic bicycle model and a Newtonian wheel stand model are discussed for plane-of-motion and out-of-plane vehicle movement, respectively. A separate controller is designed for each model by comparing different control methods. Proportional-Integral-Derivative (PID) control, optimal control, and model predictive control (MPC) are presented and applied to the models. The models are simulated from a speed of 75 m/s, being the estimated top speed of the research vehicle, up to a top speed of 150.5 m/s which is in alignment with the highest recorded speed of a dragster. The comparison of the control techniques yields MPC as superior for the bicycle model and PID as sufficient for the wheel stand model. Latency of the system is also discussed and accounted for in the system.
Donikian, Vatche, Bell, Joseph, Washington, Gregory
Effect of Adherent Rain on Vision-Based Object Detection Algorithms2020-01-01044/14/2020
Adverse weather conditions degrade the quality of images used in vision-based advanced driver assistance systems (ADAS) and autonomous driving algorithms. Adherent raindrops onto a vehicle’s windshield occlude parts of the input image and blur background texture in regions covered by them. Rain also changes image intensity and disturbs chromatic properties of color images. In this work, we collected a dataset using a camera mounted behind a windshield at different rain intensities. The data was processed to generate a set of distorted images by adherent raindrops along with ground truth data of clear images (just after a windshield wipe). We quantitatively evaluated the amount of distortion caused by the raindrops, using the Normalized Cross-Correlation and Structural Similarity methods. While most prior work in the field of rain detection and removal focuses on the image restoration aspects, they typically do not provide quantitative measures to the effect of degradation of input image quality on the performance of image-based algorithms. We quantitatively evaluated the effect of raindrop distortion on deep-learning-based object detection algorithms by comparing the detectors’ performance on the distorted images to the clear images. State-of-the-art detector algorithms were selected and used, namely, Faster Region-based Convolution Neural Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). For the overall performance of the object detection and classification algorithms, we used standard accuracy, precision, and recall measures.
Hamzeh, Yazan, El-Shair, Zaid, Rawashdeh, Samir A.
On Perception Safety Requirements and Multi Sensor Systems for Automated Driving Systems2020-01-01014/14/2020
One major challenge in designing SAE level 3-5 Automated Driving Systems (ADS) is to define requirements for the perception system that would enable argumentation for safe operation. The safety requirements on the perception system can only be fulfilled through redundancy in the sensor hardware. It is, however, a challenge to specify the redundancy that is required in the sensor system. Safe operation for an ADS is significantly more difficult compared to advanced driver assistance systems (ADAS). The safety argumentation for ADAS typically argues that in case of a failure in the sensor array a fail-silent behavior is acceptable because the human driver can take control of the vehicle back. This argumentation however is not possible when developing level 4 or higher automation. This paper investigates prerequisites for applying a systematic methodology for analyzing redundancy in a multi-sensor system and the relation to a conceptual ADS functional architecture. This analysis must address the complexity that comes with partially overlapping sensor data from different sensors and considers variations in performance and characteristics due to changes in the environmental conditions. The paper introduces the term incomplete redundancy and presents a systematic methodology for analyzing redundancy. The aim is to provide arguments for how several sensors in a system, when appropriately combined, meet an assigned safety requirement on a higher level. Each sensor will then be assigned a certain responsibility and contributes with a sub-set of information. A set of questions of importance to address as a foundation for such a methodology are defined and discussed. The definitions of redundancy and independence between sensors are discussed as well as contract-based functional safety to adapt to different environmental and operating conditions.
Cassel, Anders, Bergenhem, Carl, Christensen, Ole Martin, Heyn, Hans-Martin, Leadersson-Olsson, Susanna, Majdandzic, Mario, Sun, Peng, Thorsén, Anders, Trygvesson, Jörgen
Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models2020-01-01124/14/2020
Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.
Xiao, Wei, Zhang, Lijun, Meng, Dejian
Analysis of LiDAR and Camera Data in Real-World Weather Conditions for Autonomous Vehicle Operations2020-01-00934/14/2020
Autonomous vehicle technology has the potential to improve the safety, efficiency, and cost of our current transportation system by removing human error. With sensors available today, it is possible for the development of these vehicles, however, there are still issues with autonomous vehicle operations in adverse weather conditions (e.g. snow-covered roads, heavy rain, fog, etc.) due to the degradation of sensor data quality and insufficiently robust software algorithms. Since autonomous vehicles rely entirely on sensor data to perceive their surrounding environment, this becomes a significant issue in the performance of the autonomous system. The purpose of this study is to collect sensor data under various weather conditions to understand the effects of weather on sensor data. The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro. Two custom scenarios (static and dynamic objects) were chosen to collect sensor data operating in four real-world weather conditions: fair, cloudy, rainy, and light snow. An algorithm developed herein was used to provide a method of quantifying the data for comparison against the other weather conditions. The results from these performance algorithms show that sensor data quality degrades by an average of 13.88% for static objects and 16.16% for dynamic objects while operating in these conditions, with operations in rain proving to have the most significant effect on sensor data degradation. From this study, it is hypothesized that advancements in data processing algorithms can improve the usability of this degraded data. In future work, we seek to explore fault-tolerant sensor fusion algorithms that can overcome the effects of adverse weather.
Goberville, Nick, El-Yabroudi, Mohammad, Omwanas, Mark, Rojas, Johan, Meyer, Rick, Asher, Zachary, Abdel-Qader, Ikhlas
Research on AEB Collision Avoidance Strategy Based on Characteristics of Driver-Vehicle-Road2020-01-12134/14/2020
With the rise of intelligent transportation systems around the world, research on automobile active safety technology has gained widespread attention. Autonomous Emergency Braking (AEB) which can avoid or mitigate collision by active braking has become a hot research topic in the field of automobile. However, there are some limitations in the present AEB collision avoidance strategy, including lack of effective identification of road adhesion conditions, mismatch of active braking system parameters and imperfection of target vehicle motion information, which leads to poor collision avoidance performance on low adhesion coefficient road surface and intervention with the normal driving operation of the driver. A new collision avoidance strategy for AEB is proposed in this paper. Firstly, a new safe distance collision avoidance model is established based on the peak adhesion coefficient in real time, the performance parameters of the active braking system and the motion information of the target vehicle. Secondly, under the premise of not interfering with the driver's normal collision avoidance operation, an AEB collision avoidance strategy that can balance vehicle safety and intervention comfort is proposed. Finally, the C-NCAP scenario test is carried out on the hardware-in-loop test environment. The results show that the AEB collision avoidance strategy proposed in this paper can actively adjust the braking moment according to the current road surface attachment condition. The collision avoidance can be achieved in the most test scenarios, and the minimum distance between the self-vehicle and the target vehicle is less than 1.5 meter. In the few test scenarios, the collision can also occur at a relative speed lower than 4.2 km/h, which satisfies the active avoidance of the vehicle.
He, Ren, Zhang, Dong
A Study on the Effect of Tire Temperature and Rolling Speed on the Vehicle Handling Response2020-01-12354/14/2020
Rubber is a non-linear viscoelastic material which properties depend upon several factors. In a tire two of these factors, namely the temperature and excitation frequency, are significantly influenced by the vehicle operating conditions. In the past years, applied research studied how rubber viscoelastic characteristics affect structural and frictional tire properties. The present study focuses on how these effects interact with the vehicle handling response. Based on state of the art theory of friction, structural properties of rubber and on experimental evidence, the dependency of key tire parameters on temperature and rolling speed is established. These results are then used in combination with a single-track vehicle model to assess their impact on key vehicle parameters; as an example, the understeer coefficient, yaw resonance peak / damping and maximum acceleration are studied. Furthermore, to ensure accurate results in realistic situations, a novel tire thermodynamic model is used in combination with a detailed 14 degrees of freedom vehicle model in a numerical simulation environment. The simulations permit to study the mutual effects between tire temperature, rolling speed and vehicle dynamics. Quantitative figures are given that determine the impact on the specific vehicle handling parameters in different operating conditions. It is finally concluded that, in most cases, a higher tire temperature and / or higher rolling speed results in a degradation of the vehicle handling response.
Lugaro, Carlo, Alirezaei, Mohsen, Konstantinou, Ioannis, Behera, Abhijeet
Driver Drowsiness Behavior Detection and Analysis Using Vision-Based Multimodal Features for Driving Safety2020-01-12114/14/2020
Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis. First, an infrared camera is deployed in-vehicle to capture the driver’s facial and head information in naturalistic driving scenarios, in which the driver may or may not wear glasses or sunglasses. Second, we propose and design a multi-modal features representation approach based on facial landmarks, and head pose which is retrieved in a convolutional neural network (CNN) regression model. Finally, an extreme learning machine (ELM) model is proposed to fuse the facial landmark, recognition model and pose orientation for drowsiness detection. The DDMS gives promptly warning to the driver once a drowsiness event is detected. The proposed CNN and ELM models are trained in a drowsy driving dataset and are validated on public datasets and field tests. Comparing to the end-to-end CNN recognition model, the proposed multi-modal fusion with the ELM detection model allows faster and more accurate detection with minimal intervention. The experimental result demonstrates that DDMS is able to provide real-time and effective drowsy driving alerts under various light conditions to augment driving safety.
Li, Rui, Brand, Howard, Gopinath, Aditya, Kamarajugadda, Srivatsav, Yang, Liang, Wang, Weitian, Li, Bing
Vision System for Detecting a Small Object with Arbitrary Shape at Far Range2020-01-00944/14/2020
For one of the advanced sensing technologies for the autonomous driving, we have been working on the new vision system. It focuses on detecting the small object at far ranges. It enables to detour a vehicle by avoiding small object. This system is based on the high-resolution mono-camera with narrow FOV and the algorithms for object-detection and lane-detection. Since we have already proposed the system for the traffic cones, we proceed to a new algorithm for an unknown object with arbitrary shape. It can detect the object and estimate its range by using the information on the lane even though the shape and dimension of the object are unknown. Here the system architecture and how the algorithm to detect the object and estimate its range are described. The algorithm is at first validated by a computer-generated image. And then it is applied to real images. For the cones, the accuracy was +/- 4 [m] for the ranges between 50 and 150 [m]. For unknown objects (a tire and a cardboard), the detection was successful and the range estimation was also satisfactory.
Miyahara, Shunji, Li, Huan, Xie, Kunpeng, Bai, Jincheng
Perceptions of Two Unique Lane Centering Systems: An FOT Interview Analysis2020-01-01084/14/2020
The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance. Perceptions of adaptive cruise control (ACC) were similar across vehicles. Almost all participants mentioned benefits of comfort and safety associated with ACC use. Participants cited different benefits between the two vehicle’s implementations of lane centering. A majority of participants (75%) described comfort benefits associated with Super Cruise, while less than half (41%) cited comfort benefits associated with Pilot Assist. Only a few participants (25%) mentioned safety benefits associated with Super Cruise. Half (50%) of the participants mentioned safety benefits associated with Pilot Assist. Almost all participants cited fears of potential misuse of the system in which drivers might pay less attention to the driving task. Results suggest that drivers’ comprehension and expectation of these systems’ behavior are strongly influenced by their design philosophies, specifically in terms of the difference in hands-on versus hands-off-wheel implementation. The perceived role of the driver – as either a fallback driver or as an assisted driver - may be influenced by the design implementation.
Landry, Steven, Seppelt, Bobbie, Russo, Luca, Mehler, Bruce, Angell, Linda, Gershon, Pnina, Reimer, Bryan
Intention-aware Lane Changing Assistance Strategy Basing on Traffic Situation Assessment2020-01-01274/14/2020
Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation. Traffic situation is assessed according to the index of the motion intentions and the coupling tendency between the ego vehicle and the obstacle vehicles and the risk level is graded basing on the map with collision time. Lane keeping assist is carried out according to the assessment result of the traffic situation. Testing scenarios with the straight road and T-junction are designed and a co-simulation environment consisting of CarMaker and Mathwork Simulink is established to verify the proposed strategy in complex traffic scenes. Simulation results present an adaptive ROI and a high identification accuracy for motion intentions of the obstacle vehicles. What’s more, it shows that the traffic situation can be accurately evaluated and the ego vehicle can be effectively controlled with the appearance of the high-risk vehicles.
Wu, Jian, Liu, Sihan, He, Rui, Sun, Bohua
The Design of Safe-Reliable-Optimal Performance for Automated Driving Systems on Multiple Lanes with Merging Features2020-01-01224/14/2020
Safety function for automated driving systems including advanced driver assistance systems and autonomous vehicle systems is very important. Inside safety function, predictive judge sub-function should be designed with the consideration of more and more penetration of automated driving vehicles. This paper presents the design on multiple lanes with merging features based on the author's previous Patent JP2019-147944 using predictive time-head-way and time-to-collision maps. In the author's previous work (Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information-Published on IEEE Transactions on Intelligent Transportation Systems), a model predictive control framework was designed. Due to the difficulty to detail the sub-safety function deeply with merging features, few works are found to deal with sensor platforms focusing on rear side, and situations of merging lane side with the consideration of relative relation variations with other vehicles and road border markers. However, performance enhancement is needed assuring 100% safety-reliability-optimality and single-objectivity. Also, platforms of on-board sensors including side and rear view are needed to deal with false negative operations and false positive operations. The optimal operation line model of human factors is designed based on time-head-way (reliability), time-to-collision (safety), and combinations of time-head-way and time-to-collision (optimality). The general theory of model predictive control is used to find the target. The model based methodology is applied to solve the human factor model of risk feeling based on only time-head-way and time-to-collision for the human reaction and acceptance metric. Experimental results validated the effectiveness of the proposed approach. The model parameters can be calibrated internationally by tuning the metric of cooperativeness. The target of the predictive judge sub-function is to move the operation point to the specified area. The predictive judge sub-function on high level is decisive for regulation control to move the operation point from difficult areas to the target area in future.
Yu, Kaijiang
Obstacle Avoidance Using Model Predictive Control: An Implementation and Validation Study Using Scaled Vehicles2020-01-01094/14/2020
Over the last decade, tremendous amount of research and progress has been made towards developing smart technologies for autonomous vehicles such as adaptive cruise control, lane keeping assist, lane following algorithms, and decision-making algorithms. One of the fundamental objectives for the development of such technologies is to enable autonomous vehicles with the capability to avoid obstacles and maintain safety. Automobiles are real-world dynamical systems - possessing inertia, operating at varying speeds, with finite accelerations/decelerations during operations. Deployment of autonomy in vehicles increases in complexity multi-fold especially when high DOF vehicle models need to be considered for robust control. Model Predictive Control (MPC) is a powerful tool that is used extensively to control the behavior of complex, dynamic systems. As a model-based approach, the fidelity of the model and selection of model-parameters plays a role in ultimate performance. Hardware-in-the-loop testing of such algorithms can often prove to be complex in its design as well as in its implementation. Therefore, in this paper, we explore a less-used deployment toolchain that combines the power of ROS (Robot Operating System) for intra-robot communication with motors and sensors with the rich library of controller models in Simulink Real-Time. In particular we explore this rapid-control-prototyping in real-time to deploy Model Predictive Control for Obstacle Avoidance on a ROS-based scaled-vehicle. We found that this framework is user-friendly and contains great potential for educational and research-bed deployments - with a short development and deployment time that can fit neatly in one semester.
Bulsara, Ardashir, Raman, Adhiti, Kamarajugadda, Srivatsav, Schmid, Matthias, Krovi, Venkat N
Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies2020-01-10574/14/2020
Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems. With this DiL platform, a connected eco-operation system for the plug-in hybrid electric bus (PHEB) has been developed and tested, which can optimize the vehicle dynamics (and potentially powertrain control via smart energy management) to reduce the operational energy consumption as well as tailpipe emissions of the target PHEB. The system performance has been evaluated on the DiL platform with respect to a variety of traffic congestion levels. The results have shown that the developed system can save fuel by more than 13% while reducing the electricity consumption by 2% in the test scenarios.
Wu, Guoyuan, Brown, Dylan, Zhao, Zhouqiao, Hao, Peng, Todd, Michael, Boriboonsomsin, Kanok, Barth, Matthew, Gao, Zhiming, LaClair, Tim
A Stability-Guaranteed Time-Delay Range for Feedback Control of Autonomous Vehicles2020-01-00904/14/2020
The vehicles with level-5 autonomy (L5AVs) that have no human driver in the loop are also known as self-driving cars. L5AVs are assumed the next generation of ground transportation, which have growing attention from both industry and academia in most recent years. Most of the work related to feedback strategies of L5AVs are on developing mapping systems through a variety of sensors. These systems can be considered as an analogue to the perception and central nervous system of human drivers. For instance, innovative visualization systems are more powerful when compared to the visual perception system of a person, yet, mapping demands high computation loads. This burden causes delay in the feedback loop and thus, it might have an unfavorable influence on proper and safe control action. This study investigates the effect of time delay occurring in mapping systems on the stability of the controlled vehicle. An algorithm entitled as “Cluster Treatment of Characteristic Roots - CTCR” is used to calculate a safe delay range as a remedy for the time delay caused by mapping systems. The CTCR analysis is applied to a linearized two degree-of-freedom bicycle model for different velocities. The critical time delay values, which determines the boundary between the stability and instability of the controlled vehicle, are calculated based on the vehicle dynamics. Finally, results are drawn for a regular weave test by computer simulations, in which a non-linear vehicle model is used. The proposed approach is validated by exhibiting that a delay value outside the safe range leads the vehicle instability.
Kirli, Ahmet, Arslan, Mehmet Selçuk
Decision Making and Trajectory Planning for Lane Change Control Inspired by Parallel Parking2020-01-01344/14/2020
Lane-changing systems have been developed and applied to improve environmental adaptability of advanced driver assistant system (ADAS) and driver comfort. Lane-changing control consists of three steps: decision making, trajectory planning and trajectory tracking. Current methods are not perfect due to weaknesses such as high computation cost, low robustness to uncertainties, etc. In this paper, a novel lane changing control method is proposed, where lane-changing behavior is analogized to parallel parking behavior. In the perspective of host vehicle with lane-changing intention, the space between vehicles in the target adjacent lane can be regarded as dynamic parking space. A decision making and path planning algorithm of parallel parking is adapted to deal with lane change condition. The adopted algorithm based on rules checks lane-changing feasibility and generates desired path in the moving reference system at the same speed of vehicles in target lane. Compared to algorithm for static parking space, the uncertainty of the space between moving vehicles and host vehicle dynamics raises stricter requirements for algorithms. Works are conducted to deal with dynamically changing scenarios, such as design of safety zone and exit conditions to avoid collision. Simulation under PreScan-Simulink environment shows that the proposed method outperforms in lane change scenarios and achieves strong robustness to inter-vehicle dynamics.
Yu, Liangyao, Ru, Ze, Lu, Zhenghong, Liang, Guanqun, Xiong, Cenbo, Lanie, Abi, Wang, Ruyue
Effect of Space Weather on Autonomous Vehicle Navigation2020-01-01404/14/2020
Autonomous vehicle systems integrate multiple information systems. Navigation is reliant on Global navigation satellite systems (GNSS) such as the global positioning systems (GPS) which are supported by a satellite network. However, satellites and radio signals are subject to interference from sunspots. Sunspots happen on regular cycles at varying strengths but their occurrence can’t be exactly predicted. The likelihood of a severe solar event is roughly twelve percent per decade; consequently, solar events are likely to impair navigation. Results will show the probability of each event and its impact on autonomous vehicle navigation. In the worst case scenario, satellites could even be permanently damaged by severe sunspots. As autonomous vehicles become a more significant portion of the economy, it is necessary that they have resilience to operate in extreme conditions. Alternative navigation procedures are proposed to enhance the resiliency of autonomous vehicles. Artificial intelligence related to place identification with relative geographic navigation and storage of most common routes is an option.
Hoback, Alan
Simulation of Curved Road Collision Prevention Warning System of Automobile Based on V2X2020-01-07074/14/2020
The high popularity of automobiles has led to frequent collisions. According to the latest statistics of the United Nations, about 1.25 million people worldwide die from road traffic accidents each year. In order to improve the safety of vehicles in driving, the active safety system has become a research hotspot of various car companies and research institutions around the world. Among them, the more mature and popular active security system are Forward Collision Warning(FCW) and Autonomous Emergency Braking(AEB). However, the current active safety system is based on traditional sensors such as radar and camera. Therefore, the system itself has many limitations due to the shortage of traditional sensors. Compared to traditional sensors, Vehicle to Everything (V2X) technology has the advantages of richer vehicle parameter information, no perceived blind spots, dynamic prediction of dangerous vehicle status, and no occlusion restriction. In order to overcome the many shortcomings of the existing anti-collision warning system and strategy, this paper proposes a curved road collision prevention warning strategy based on V2X technology. Through V2X technology, the state information released by the neighboring car and the road environment information issued by the roadside unit are obtained. Using the above information and the state information of the vehicle, the relative positional relationship between the car and the neighboring car is dynamically predicted in real time, and then a two-degree-of-freedom dynamic collision time model and a two-degree-of-freedom collision time threshold model are proposed and designed. Finally, based on the output parameters of the above model, a two-degree-of-freedom curved road collision prevention warning system of automobile based on V2X technology is proposed, and a layered early warning mechanism is established. Through the PreScan environment, the typical working conditions and early warning strategy models are built by Matlab & Simulink, and the simulation of the early warning strategy is completed.
Li, Xuanhe, Wu, Jian, He, Rui, Zhu, Bing, Zhao, Jian, Zhou, Hang
Data-Driven Confidence Model for ADAS Object Detection2020-01-06954/14/2020
The majority of road accident is due to human error. Advanced Driver Assistance System (ADAS) has the potential to reduce human error and improve driving safety. Customers have shown a growing acceptance for ADAS technology. With the rising demand for safety and comfortable driving experience, the global market for ADAS is expected to grow to $67 billion by 2025. A reliable ADAS system requires an accurate and robust object-detection system. There is often a trade-off in tuning the system. On one hand, miss-detection can cause accidents; on the other hand, false-detection can result in ghost-braking and harm the driving experience. The ADAS system can access various information from different sources. However, a unified confidence model, which combines different indicators, has not been much studied in the literature. In this paper, we propose a data-driven method, which utilizes the features from radar, camera and the tracking system to produce a high-level confidence model. In addition, different regions regarding the ego vehicle usually have different emphases for detection error based on the system design requirements. And therefore, we can tune towards the design requirements by change the threshold of the classifier based on the region of interest. The proposed method was validated with real-world driving data and shown a better performance based on the design requirement of the Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB) functions.
Yang, Hang, Zhang, Darui, Wang, Daihan, Zhou, Jianguang
Secure Vehicular Communication Using Blockchain Technology2020-01-07224/14/2020
The cars we drive are rapidly transforming. Connected vehicles in the context of the Advanced Driver Assistance System or Autonomous Vehicles are about to change the way we drive cars. Connected Vehicles are futuristic vehicles that can interact with other vehicles for passing on information such as, mapping and localization, information about road traffic and driving behaviour. However, such vehicles, particularly the autonomous ones, are prone to a variety of attacks including cyber-attacks. These malicious attacks can intrude a vehicle that not only endangers the vehicles safety, but also the life of passengers and the nearby environment. Thus, identifying and eliminating these attacks for providing a secure communication environment is of great need. Also, all the existing methods for vehicular communication rely on a centralized server which itself invite massive cyber-security threats. These threats and challenges can be addressed by using the Blockchain (BC) technology, where each transaction is logged in a decentralized immutable BC ledger. In this work, we show how BC can facilitate communication between connected vehicles to send and receive information while assuring the security of all the vehicles participating in the BC network. First, we developed an application for the blockchain based less-complex Proof-of-Work consensus method that allows the vehicles to transfer information in a secured manner. Second, we demonstrate the working of the application using raspberry pi board that act as vehicles mounted with sensors and two computers that act as blockchain network. Finally, we discuss the advantages and disadvantages of blockchain based vehicular communication and the integration of the blockchain with VANET as well.
M, Vidya Krishnan, Koduri, Rajesh, Nandyala, Sivaprasad, Manalikandy, Mithun
Development of a Procedure to Correlate, Validate and Confirm Radar Characteristics of Surrogate Targets for ADAS Testing2020-01-07164/14/2020
Surrogate targets are used throughout the automotive industry to safely and repeatably test Advanced Driver Assistance Systems (ADAS) and will likely find similar applications in tests of Automated Driving Systems. For those test results to be applicable to real-world scenarios, the surrogate targets must be representative of the real-world objects that they emulate. Early target development efforts were generally divided into those that relied on sophisticated radar measurement facilities and those that relied on ad-hoc measurements using automotive grade equipment. This situation made communication and interpretation of results between research groups, target developers and target users difficult. SAE J3122, “Test Target Correlation - Radar Characteristics”, was developed by the SAE Active Safety Systems Standards Committee to address this and other challenges associated with target development and use. J3122 addresses four topics. First, it describes standardized equipment and procedures for making various types of calibrated radar measurements using automotive grade equipment, with minimal measurement site restrictions. Second, a correlation procedure is provided that is used to define validity regions and properties of representative real-world objects. Third, a validation procedure is provided for comparing candidate targets against measurements of representative objects using an objective correlation score. Finally, a confirmation procedure is provided for checking in-use targets to verify that they continue to be acceptable for testing. This paper describes each of these topics as well as the process development.
Silberling, Jordan, Nicols, George, Buller, William, Lenkeit, John
LiDAR-Based Urban Autonomous Platooning Simulation2020-01-07174/14/2020
The technological advancements of Advanced Driver Assistance Systems (ADAS) sensors enable the ability to; achieve autonomous vehicle platooning, increase the capacity of road lanes, and reduce traffic. This article focuses on developing urban autonomous platooning using LiDAR and GPS/IMU sensors in a simulation environment. Gazebo simulation is utilized to simulate the sensors, vehicles, and testing environment. Two vehicles are used in this study; a Lead vehicle that follows a preplanned trajectory, while the remaining vehicle (Follower) uses the LiDAR object detection and tracking information to mimic the Lead vehicle. The LiDAR object detection is handled in multiple stages: point cloud frame transformation, filtering and down-sampling, ground segmentation, and clustering. The tracking algorithm uses the clustering information to provide position and velocity of the Lead vehicle which allows for vehicle platooning. This paper covers the LiDAR object detection and tracking algorithms as well as the autonomous platooning control algorithms which were tested in a simulation environment. Test results illustrate that the Follower vehicle was able to attain the autonomous platooning based on the LiDAR data.
Alzu'bi, Hamzeh, Tasky, Tom
Runtime Active Safety Risk-Assessment of Highly Autonomous Vehicles for Safe Nominal Behavior2020-01-01074/14/2020
Fatal crashes involving automated driving systems, has been raising the concern of minimum standard requirement for safety, reliability and performance required for Autonomous Driving System (ADS)/Advanced Driver Assistance System (ADAS) before this cutting-edge technology takes on public roads. Hence, in order to ensure necessary safety requirements of ADS/ADAS systems we propose a runtime active safety assurance module known as SConSert. SConSert performs dynamic risk assessment of “Sensing, Planning and Action module of ADS/ADAS”; to provide minimal risk maneuver in any given driving scenario. The dynamic risk assessment of ADS/ADAS system is based on the operational design domain (ODD) knowledge of the driving scenario plus the sensor capability, ADS/ADAS algorithm requirement and capability, and finally smooth and collision free maneuver requirement. So, the main concept behind SConSert is runtime derivation of situational and conditional set of contracts for a given driving scenario and ADS/ADAS system ODD; fulfillment or violation of which can help in runtime dynamic risk assessment of ADS/ADAS to plan minimal safe behavior such that necessary safety requirements can be achieved. Finally, through experiment we show that proposed runtime active assurance safety module can handle complex driving scenario, and present simulation and experimental results that emphasizes the importance of the proposed runtime safety assurance module and shows that the proposed system is capable of performing runtime dynamic risk assessment in order to keep the automated driving systems always within the safe sate that is the automated driving system always perform within its ODD.
Rathour, Swarn Singh, Ishigooka, Tasuku, Otsuka, Satoshi, MARTIN, RAUL
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