Browse Topic: Driver assistance systems

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Test:15/2SAE-PP-078102/15/2023
Test:15/2
Mutagaana, Festo
Test Preprint 20220628 By Madhukar DongariSAE-PP-025996/28/2022
Test Preprint 20220628 By Madhukar Dongari
Mutagaana, Festo
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.
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
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
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
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
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
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
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
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
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
Reference Test System for Machine Vision Used for ADAS Functions2020-01-00964/14/2020
Advanced Driver Assistance Systems (ADAS) like Lane Departure Warning (LDW) and Lane Keep Assist (LKA) have been available for several years now but has experienced low customer acceptance and market penetration. These deficiencies can be traced to the inability of many of the perception systems to consistently recognize lane markings and localize the vehicle with respect to the lane markings in the real-world with poor markings, changing weather conditions and occlusions. Currently, there is no available standard or benchmark to evaluate the quality of either the lane markings or the perception algorithms. This work seeks to establish a reference test system that could be used by transportation agencies to evaluate the quality of their markings to support ADAS functions that rely on pavement markings. The test system can also be used by designers as a benchmark for their proprietary systems. To support this development, an extensive video dataset was collected at different times of day and weather conditions on various roads in Central Texas. The videos were evaluated on different state-of-the art lane detection algorithms and their performance was ranked based on a set of metrics specifically developed for evaluating the effectiveness of the lane estimation system. The test scenarios are comprised of a set of roadways and environmental features, as well as the pavement marking presence and luminance variables. A systems approach is presented by correlating the algorithm performance data to the environmental factors, lane marking types, color, material, and the retroreflectivity of pavement markings.
Nayak, Abhishek, Rathinam, Sivakumar, Pike, Adam, Gopalswamy, Swaminathan
Evaluation of Methods for Identification of Driving Styles and Simulation-Based Analysis of their Influence on Energy Consumption on the Example of a Hybrid Drive Train2020-01-04434/14/2020
Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption. The submitted paper deals with the analysis of different methods and measurement systems for the identification and classification of driver profiles as well as with their potential to optimize both vehicle driving behavior and energy consumption on the example of a hybrid drive train. A literature research results in a number of different approaches of evaluation, which are analyzed, linked and adapted in the publication. As a result, an evaluation of the connection between different methods of driver profile determination is given. Data collection and interviews have been performed during twenty test drives on a defined route profile with different measurement systems and methods. The acquired data form the basis for a comparison and an analysis of a comprehensive driving style classification. Subsequently, a framework for computer-aided investigations of the influences of driver behavior on the control of drive trains is established by use of an existed simulation model of a hybrid drive train. Finally, a driver model is implemented based on the learnings out of analyzing the measurements and surveys. The evaluation of the measurement campaigns delivers detailed information about vehicle longitudinal acceleration behavior in different driving scenarios. This information is used to classify the individual driving styles into the types calm, normal and aggressive. This driving style-related information can be integrated into the control strategy of a hybrid power train to support operation strategy optimization regarding both driver satisfaction and reduction of energy-, respectively fuel consumption.
Domijanic, Marko, Hirz, Mario, Pucher, Gregor
GNSS-Based Lane Keeping Assist System Using Model Predictive Control and Time Delay Compensation2020-01-10234/14/2020
In recent decades, research and development in the field of autonomous vehicles have rapidly increased throughout the world, and autonomous driving technologies have begun to be applied to mass production vehicles. Especially recently, even affordable mass production vehicles have begun to be equipped with some autonomous driving systems such as a Lane Keeping Assist (LKA) system. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage or blurred lane markings. To overcome this limitation, we have developed Global Navigation Satellite System (GNSS)-based LKA systems, which are not affected by the surrounding environment such as weather and the condition of lane markings. In our latest study, we applied Model Predictive Control (MPC) to our GNSS-based LKA system so as to enhance lane-keeping performance. We then revealed that the GNSS-based LKA system with MPC had low robustness regarding the time delay of a GNSS and that countermeasures for the time delay were necessary. In this paper, we apply Smith predictor-like Time Delay Compensation (TDC) to compensate for the time delay. The TDC predicts the current state variables from the past sensor signals based on the vehicle dynamics. We demonstrate that the TDC stabilizes the LKA system even when the GNSS has a time delay in a simulation. Furthermore we add another TDC to compensate for the time delay of Electrical Power Steering (EPS) with the aim of reducing the oscillation of the steering wheel angle. Finally, we evaluate the lane keeping performance in a real-vehicle experiment on a snow-covered highway.
Tominaga, Kenta, Takeuchi, Yu, Kitano, Hiroaki, Tomoki, Uno, Quirynen, Rien, Cairano, Stefano
Prevention of Snow Accretion on Camera Lenses of Autonomous Vehicles2020-01-01054/14/2020
With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses. In the experiments, snow is formed using a novel snow gun inside a walk-in cold room connected to a wind tunnel that can reach wind speeds of up to 40 mph. It is observed that the air flow over the lens significantly reduces the accretion of snow on the lens and could maintain the lens clean for up to 20 mph wind velocities. However, at LWC values of approximately 28%, the stickiness of the snowflakes increases, enhancing snow accretion on the lenses and translating to significant loss of sight. The high stickiness of the snowflakes along with high wind speeds leads to increased blockage of the AV lenses.
Mohammadian, Behrouz, Sarayloo, Mehdi, Heil, Jamie, Sojoudi, Hossein, Robertson, Michael, Hong, Haiping, Tran, Tommy, Patil, Sunil, Krishnan, Venkatesh
Robust Sensor Fused Object Detection Using Convolutional Neural Networks for Autonomous Vehicles2020-01-01004/14/2020
Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module. The proposed MSFD system employs the principles of artificial intelligence beyond simple comparison of data variance of the sensors. And then, its performance is further improved by using the temporal correlation information with an adaptive threshold scheme. The proposed system is evaluated with the collected video data (6,854 image frames with 18,918 labeled objects). Based on the laboratory testing and in-vehicle validation, the proposed system demonstrates its high accuracy for detecting any size of objects in real-world data.
Park, Jungme, Jayachandran Raguraman, Sriram, Aslam, Aakif, Gotadki, Shruti
IMU Contributions to Navigation and Safety for ADAS and Autonomous VehiclesTBMG-361513/1/2020
Two of the fastest growing areas in automotive engineering are Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. To learn about the contribution of Inertial Measurement Units (IMU) to automobile navigation systems for these two applications, we interviewed Mike Horton, CTO of ACEINNA, Inc. (Boston, MA).
Modification of Density-Based Clustering and Threshold Adjustment Detection-Tracking Integration Algorithm for 77 GHz Automotive Radar2020-01-50272/24/2020
In this paper, a modification of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed based on real-world high resolution point clusters, which is obtained by 77-GHz frequency modulated automotive radar through CFAR technology. The modification of DBSCAN includes introducing normalized DBSCAN pre-processing method and a scale factor for dimension stretching/compression, which makes the target have similar measurement in three different dimensions of two-dimensional Cartesian coordinate system and velocity dimension, a The scale factor can adjust the sensitivity of DBSCAN algorithm to velocity dimension to deal with velocity expansion problem. Based on the clustering results, this paper proposes CFAR-JPDA algorithm, which integrates detection and tracking, and proposes a new extended Kalman filter state equation and measurement equation for road moving targets. In terms of data association, the modified JPDA algorithm is proposed, and the shape information of the target is used to confirm the measurement twice, which improves the accuracy of the target association.
Houyuan, Zhang, Yicheng, Jiang, Zhenwei, Miao, Gang, Ye, Bing, Wang
Democratize AV Technology!20AVEP01_041/1/2020
Aptiv's new generation of open-sourced architectures based on a few central processors aims to speed AV adoption. CTO Glen DeVos explains. The software-intensive, electrified and increasingly automated vehicle will define the 2020s. Its rise is driving both the industry-wide re-thinking of electrical architectures and the growth of engineering employment behind it. At the forefront of this trend is Aptiv, the technology Tier 1 spun off from Delphi in 2017. It now has more than 19,000 engineers among its 160,000 staff, comprising one of the highest engineer-to-employee ratios among large suppliers. “We've been adding about 1,500 engineers per year, primarily in software and systems engineering, at our 15 major technical centers,” said CTO Glen DeVos. These resources, he noted, will help Aptiv accelerate its customers' development of new vehicle platforms with greater active-safety capability, including automated-driving functionality.
Brooke, Lindsay
Virtual Assessment of Automated Driving: Methodology, Challenges, and Lessons Learned12-02-04-002012/18/2019
Automated driving as one of the most anticipated technologies is approaching its market release in the near future. Since several years, the research in the automotive industry is largely focused on its development and presents well-engineered prototypes. The many aspects of this development do not only concern the function and its components itself, but also the proof of safety and assessment for its market release. It is clear that previous methods used for the release of Advanced Driver Assistance Systems are not applicable. In contrast to already released systems, automated driving is not restricted to a certain field of application in terms of driving scenarios it has to take action in. This results in an infeasible amount of required testing and unforeseeable scenarios the function can face throughout its lifetime. In this article, we show a scenario-based approach that promises to overcome those challenges. In contrast to previous methods, it includes virtual test domains in a verified way to diminish the demand for real-world testing. Local verification of certain scenarios from real-world testing enables virtual variation in a local test space, and thus contributes to the test volume. The approach is implemented, evaluated, and shows first promising results. An intentional evaluation against a corner case-a scenario where the use of virtual test domains is challenged-from the set of scenarios reveals current weak spots. These are transformed into lessons learned, proposed solutions, and future work for enhancements. With the aid of these, the suggested methodology is a promising candidate to overcome the challenges of evaluating automated driving functions.
Wagner, Sebastian, Knoll, Alois, Groh, Korbinian, Kühbeck, Thomas, Watzenig, Daniel, Eckstein, Lutz
Road Parameter-Based Driver Assistance System for Safe Driving12-02-04-001912/17/2019
One of the key aspects of Advanced Driver Assistance Systems (ADAS) is ensuring the safety of the driver by maintaining a safe drivable speed. Overspeeding is one of the critical factors for accidents and vehicle rollovers, especially at road turns. This article aims to propose a driver assistance system for safe driving on Indian roads. In this regard, a camera-based classification of the road type combined with the road curvature estimation helps the driver to maintain a safe drivable speed primarily at road curves. Three Deep Convolutional Neural Network (CNN) models viz. Inception-v3, ResNet-50, and VGG-16 are being used for the task of road type classification. In this regard, the models are validated using a self-created dataset of Indian roads and an optimal performance of 83.2% correct classification is observed. For the calculation of road curvature, a lane tracking algorithm is used to estimate the curve radius of a structured road. The road type classification and the estimated road curvature values are given as inputs to a simulation-based model, CARSIM (vehicle road simulator to estimate the drivable speed). The recommended speed is then compared and analyzed with the actual speeds obtained from subjective tests.
Addanki, Sai Charan, Sowmya Vasuki, J., Venkataraman, Hrishikesh
Application Study of Blind Spot Monitoring System Realized by Monocular Camera with CNN Depth Cues Extraction Approach12-02-04-001612/17/2019
The image from monocular camera is processed to detect depth information of the obstacles viewed by the rearview cameras of vehicle door side. The depth information recognized from a single, two-dimensional image data can be used for the purpose of blind spot area detection. Blind spot detection is contributing to enhance the vehicle safety in scenarios such as lane-change and overtaking driving. In this article the depth cue information is inferred from the feature comparison between two image blocks selected within a single image. Convolutional neural network model trained by deep learning process with good enough accuracy is applied to distinguish if an obstacle is far or near for a specified threshold in the vehicle blind spot area. The application study results are demonstrated by the offline calculations with real traffic image data.
Guo, Yuxiang, Kumazawa, Itsuo, Kaku, Chuyo
Q&A19TOFHP12_1312/1/2019
Together with its many partners, ZF supplies camera and radar technology and advanced components for both the passenger car and commercial truck markets, the latter being especially suited for the move to more complex driver-assistance systems, according to Dan Williams, director of ADAS & Autonomy at ZF. “The business case in commercial vehicle for reduction in driver hours of service, fuel cost reduction and safety have strong economic incentives to adopt ADAS/automated driving technology. Additionally, the regulations placed on the industry will require our customers to utilize certain solutions,” he said. ZF is working on both highly automated “revolutionary” systems and on “evolutionary” driver-assistance systems that are increasingly complex, he said, citing the supplier's OnTraX lane keep assist that will launch in 2020 with its first major OEM customer. Williams spoke with TOHE at the recent NACV Show in Atlanta, and he's scheduled to participate in a Commercial Vehicle Safety technical session at the SAE Government/Industry Meeting taking place January 22-24, 2020, in Washington, DC (www.sae.org/attend/government-industry/).
Gehm, Ryan
Connectivity in 2 Wheelers: Opportunities and Challenges2019-28-243711/21/2019
Mobility is undergoing a “horses to cars”-sized shift that will reverberate across business and society for generations. Future of Mobility is mainly driven by 4 main pillars viz. Connected, Electrified, Automated and Shared Driving. With advancement in Communication Technology supplemented by huge customer base, Connectivity has proven to deliver better Services to the End-user. Connected Mobility is going to be the next Big Thing in the Mobility Arena. In this paper, we will try to qualitatively explore what Connected Mobility is all about and what it has to offer in terms of - Opportunities on one side as well as new challenges that were never witnessed in the realm of Mobility in the Past, with focus on the 2 wheeler segment. This paper focuses on Opportunities in terms of Location Based services, Vehicle Management, Data Analytics, Infotainment and possible Business scenarios and Models as well as challenges in Terms of Security and Data Ownership
Rao, Sathyanarayana
Vision Based Solution for Auto-Maneuvering of Vehicle for Emerging Market2019-28-251711/21/2019
Advance Active Safety Systems play a preventive role in mitigating crashes and accidents by providing warning, additional assistance to the driver and maneuverability of vehicle by itself. Some of the features include forward collision warning system and lane departure warning system activate a warning alert when potentially dangerous situations are detected. These active safety features present in developed markets work with Fusion based algorithm combining Radar, Lidar, Camera, Ultrasonic sensor’s input. Application of these algorithms are Intelligent Cruise Control, Collision avoidance, parking assistance, identify pedestrian etc. The complexity of the algorithm, cost of the control unit and road infrastructure are hindrance to emerging market. The solution presented in this paper is towards camera-based solution, describing the method to determine the predictive path, that is obstacle free space and use the predictive space to navigate or steer. This paper focuses on vehicle maneuverability in poor road infrastructure (lane irregularities or no lane marking) by using only cameras.
Bose, Souvik, Singh, Ashwani Kumar, Singampalli, D V Ram Kumar, lalwani, Chandraprakash
A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling09-07-02-000911/14/2019
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events. The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.
Gao, Jun, Yi, Jiangang, Zhu, Honghui, Murphey, Yi Lu
Unsettled Topics Concerning Automated Driving Systems and the Transportation EcosystemEPR201900511/5/2019
Over the last 100 years, the automobile has become integrated in a fundamental way into the broader economy. A broad and deep ecosystem has emerged, and critical components of this ecosystem include insurance, after-market services, automobile retail sales, automobile lending, energy suppliers (e.g., gas stations), medical services, advertising, lawyers, banking, public planners, and law enforcement. These components - which together represent almost $2 trillion of the U.S. economy - are in equilibrium based on the current capabilities of automotive technology. However, the advent of autonomous vehicles (AVs) and technologies like electrification have the potential to significantly disrupt the automotive ecosystem. The critical cog governing the rate and pace of this shift is the management of the test and verification of AVs. In this SAE EDGE™ report, six senior industry leaders in the impacted ecosystems essay articles which describe sectors of the current automotive ecosystem and the manner in which AV technology can potentially reshape them - providing a mosaic of the massive infrastructure shifts which will be required to absorb AV technologies. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.
Razdan, Rahul
Analysis of Accelerator Hardware for Autonomous Vehicles and Data Centers2019-01-261510/22/2019
The development of Autonomous Vehicles (AV) has become a popular subject in academia and industry. Companies and cities are quickly realizing the opportunities that AVs can generate from Mobility as a Service to traffic safety. The challenges for the infrastructure to incorporate AVs as a viable transportation source are immense, from an outdated infrastructure to radical Smart-City designs. Historically, the transportation infrastructure has faced challenges from underfunding, economics, and much needed improvements. With the current infrastructure unable to support many of the services required by a fully connected network, a transformation will be necessary to meet growing mobility needs. The role of accelerating technology in data centers are key for production operations among industry leaders such as Amazon and Microsoft for real-time processing. The same accelerating technology that has successfully impacted data centers will play the same role in much smaller micro data centers (mDC) for Smart-City design in the transportation infrastructure. These mDCs and Edge computing sites will be tasked with the latency, tasking caching and offloading (TCO), and processing of millions of connected vehicles simultaneously. With the recent upgrade of 5G from 4G wireless connectivity will invariably provide lower latency to Edge computing devices used in real-time applications. This paper provides an analysis of accelerator technology for real-time processing in the transportation infrastructure. Accelerator hardware such as FPGAs, GPUs, and ACISs will be highlighted from current research that support real-time capabilities. As the popularity of AVs and a connected network continues to grow, the role of accelerator technology will enable large scale real-time processing in AVs and the transportation infrastructure.
Brown, Kyle W.
Cyberattacks and Countermeasures for Intelligent and Connected Vehicles07-12-01-000510/14/2019
ICVs are expected to make the transportation safer, cleaner, and more comfortable in the near future. However, the trend of connectivity has greatly increased the attack surfaces of vehicles, which makes in-vehicle networks more vulnerable to cyberattacks which then causes serious security and safety issues. In this article, we therefore systematically analyzed cyberattacks and corresponding countermeasures for in-vehicle networks of intelligent and connected vehicles (ICVs). Firstly, we analyzed the security risk of ICVs and proposed an in-vehicle network model from a hierarchical point of view. Then, we discussed possible cyberattacks at each layer of proposed network model. After that, we provided an overview of the state of the art of the potential countermeasures against cyberattacks, such as secure hardware architecture, encryption and authentication, network firewall, intrusion detection system, and secure Firmware over the air (FOTA), and then a reasonable defense mechanism is proposed. At last, some challenges and future works related to cyberattacks against ICVs were discussed. This article aims to review the cyberattacks for ICVs and suggest comprehensive security countermeasures.
Luo, Feng, Hou, Shuo
Active Safety System for Connected Vehicles12-02-03-001310/14/2019
The development of connected-vehicle technology, which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure locations share their sensors’ data. In this article, we propose a new system where cameras installed on multiple vehicles and infrastructure locations share and fuse their visual data and detected objects in real time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology. In our proposed system the vehicle receiving the visual data from an adjacent vehicle fuses the received visual data with its own camera views to create a much richer visual scene. We conducted several experiments across a pair of vehicles equipped with DSRC devices and our proposed system. These experiments demonstrated that our system achieves high accuracy, low delay, and improved safety.
Al-Qassab, Hothaifa, Pang, Su, Al-Qizwini, Mohammed, Kent, Daniel, Radha, Hayder
An Efficient Algorithm for Extracting Structured Information from Different Vision Objects in Autonomous Vehicle2019-28-007410/11/2019
This is the age of technology and innovation. To facilitate and make human life easier, the technologist and scientist move for the innovation of automotive car that can travel towards its given destination without any input from the user. This paper focus on the accurate path tracking performance of a vehicle, and the line detection of an image using the Hough transform algorithm which finds the hidden straight line in larger amount of data. The captured image from the camera is processed through the proposed edge detector algorithm with the least square method and Kalman filter to get an accuracy of nearby edges. The advantage of the proposed algorithm is the reliability of data which paves the way for accurate prediction of the state estimation of vehicle. The simulation test was done with the existing canny edge detection algorithm and the proposed algorithm, which shows the 20% increase in the accuracy of the image using the proposed method. The proposed algorithm has been validated with minimal distance of 1 km in off real time traffic condition with minimum obstacles.
Nagarajan, Meenakshi, Thangaraju, Shanmuganathan
An ADAS Feature Rating System: Proposing a New Industry Standard2019-24-025110/7/2019
More than 90% of new vehicles include Advanced Driving Assistance Systems that offer features such as Lane Keep Assist and Adaptive Cruise Control [1]. These ever-improving vehicle systems present a great opportunity to increase driving safety and reduce the number of roadway deaths and injuries. Indeed, they are already having a positive effect. However, the wide variety of features offered in the marketplace can be confusing to consumers, who may not clearly understand their vehicles’ true capabilities and limitations, or have an easy way of comparing system performance between vehicle models. This lack of information has the potential to reduce the safety gains of ADAS features by increasing the risk of improper use. To encourage transparency in the marketplace and thus engender the maximum positive effect of ADAS technologies, this paper proposes a five-level rating system, which utilizes diamonds to denote significant milestone achievements in vehicle system performance. The rating charts resulting from this system describe gradients of performance within criteria addressed by certain foundational ADAS features. Presented here in its initial stage of development, this rating system will require continued refinement. We therefore encourage the community of automotive safety organizations to take up the mantle by establishing and performing test protocols for assigning standardized ADAS feature performance ratings. We believe that the result of this effort, a common method for understanding and comparing ADAS performance, promises to deliver a beneficial level of clarity to the industry and consumers.
Heeren, David, Gradu, Mircea
Continental Tire rolls towards a smart-and-connected future19AUTP10_0910/1/2019
It is difficult to become emotional about tires except when they-literally-let us down. But tires are set to get smarter, communicate more effectively and react to changing road conditions. Each of these will play a role in meeting the demands of autonomous- and electric-vehicle (AV/EV) development. “They will be tires that take care of themselves and provide highly supported driving,” said Dr. Philipp Struck, Continental Tire's head of tire line development. “Mobile data will be essential as we become more autonomous and drive more EVs.”
Birch, Stuart
EDITORIAL: AI, ADAS & AVs-oh my!19TOFHP08_068/1/2019
Active safety and advanced driver-assistance systems (ADAS), along with increasingly sophisticated artificial intelligence (AI) platforms-are the building blocks essential to climbing the SAE levels of automation. Acquisitions, partnerships and advanced-technology demonstrations in these areas are occurring at a dizzying rate, as the industry has set its sights on Level 4 (L4) automated vehicles (AVs). One major in-process merger involves two heavyweights in this space, ZF and WABCO. In June, WABCO shareholders approved the acquisition by ZF, which both companies expect to finalize in early 2020. Importantly, the acquisition will expand ZF's expertise to include commercial vehicle (CV) braking solutions for the first time-a significant capability for the control of automated driving functions like emergency braking maneuvers of trucks and trailers. This fully integrated system approach is expected to accelerate the introduction of autonomous CV functions moving forward.
Gehm, Ryan
SAE Truck & Off-Highway Engineering: August 201919TOFHP088/1/2019
Electric machines find their niche Smaller machines working in cities are particularly ripe for electrification, but most off-highway applications will retain the internal-combustion engine. Big data, big benefits Data mining helps users and equipment developers use data from on-vehicle sensors to work more efficiently. Data connectivity in harsh environments Ensuring high-speed data transmission requires OEM designers to think more about components, placement and the impact of environmental conditions early in design. Trucks with intuition Perceptive Automata partners with Volvo Trucks to demonstrate AI technology that can determine when pedestrians will cross the road. Change is afoot Cummins and its CTO help focus the 2019 SAE COMVEC on the fast-shifting technology landscape that faces both on- and off-highway sectors. Editorial AI, ADAS & AVs-oh my! Holistic hydraulic designs add more sensors, controls DSD helps 'balance the books' for buying electric truck technology JCB prepared to meet EU Stage V emissions Protecting a cyber-physical remote diagnostic communication system against cyberattacks SKF employs digital tools to enhance bearing design, performance New Mercedes-Benz Actros loaded with ADAS, active safety tech Q&A Peloton CEO Josh Switkes explains Level 4 'automated following' platooning
German University Studying Assisted-Driving ‘Handoff’19AVEP07_087/1/2019
Germany's Kempten University Adrive Living Lab will feature latest aVDS driving simulator to research ADAS stress levels. Researchers at Germany's Kempten University of Applied Sciences plan to embark this year on autonomous vehicle (AV) and advanced driver-assist system (ADAS) programs that will include investigation of the safest ways to achieve handover of a vehicle when control is returned to a human driver. To do so, it is installing an AB Dynamics advanced Vehicle Driving Simulator (aVDS) that will help measure driver stress levels if the vehicle is not capable of performing a particular maneuver. The aVDS has been designed to provide high level R&D capabilities specifically for autonomous systems. The university has established its Adrive Living Lab with the third-generation simulator helping to facilitate the study of the driver's interaction with autonomous systems and their effect on drivers' perceived safety and comfort. The program has identified these two areas as the most important for consumer acceptance and enthusiasm.
Birch, Stuart
ZF's Current Work Builds for the EV, AV Future19AVEP07_097/1/2019
The Tier-1 giant's “vision” for improving future mobility leverages its latest safety and chassis-development innovations. As the product-development landscape for light-vehicle electrification and automated-driving technologies becomes less cluttered, it's apparent that established automotive Tier 1 mega-suppliers are intent on merging their established competencies with whatever new product lines are required in the electrified, automated future…whenever it comes. This strategy was recently on display at a technology background event conducted in the U.S. by ZF. Long regarded for its transmission, driveline and chassis expertise, ZF showed customers and journalists various prototype systems intended to merge current-generation components to create higher-level functionality-and value.
Visnic, Bill
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