Browse Topic: Cooperative driving automation

Items (30)

Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202107 (Current)

7/16/2021

This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users. Vehicles and infrastructure elements engaged in cooperative automation may share information, such as state (e.g., vehicle position, signal phase), intent (e.g., planned vehicle trajectory, signal timing), or seek agreement on a plan (e.g., coordinated merge). Cooperation among multiple participants and perspectives in traffic can improve safety, mobility, situational awareness, and operations. However, nothing in this document is intended to suggest that driving automation requires such cooperation in order to be performed safely. Cooperative strategies may be enabled by the sharing of information in a way that meets the needs of a given application. The needs may be expressed in terms of performance characteristics, such as latency, transmission mode (e.g., one-way, two-way), range, privacy and security, and information content and quality. There are several potential technologies for communicating information between the subject vehicle and other participants. This document focuses on application-oriented functionality and does not imply the need for or require any specific functionality associated with communications protocols or the open systems interconnection model layers in a protocol stack. This document addresses the operational and tactical timescales of dynamic driving on ADS-operated vehicles, and excludes strategic functions such as trip scheduling and selection of destinations and waypoints. This information report is intended to facilitate communication and awareness for the design and anticipated development and validation of cooperative driving automation.

Cooperative Driving Automation(CDA) 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

Correlation between Sensor Performance, Autonomy Performance and Fuel-Efficiency in Semi-Truck Platoons

2021-01-00644/6/2021

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.

Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202005 (Historical)

5/7/2020

Cooperative Driving Automation(CDA) Committee

Platooning Vehicles Control for Balancing Coupling Maintenance and Trajectory Tracking - Feasibility Study Using Scale-Model Vehicles

2020-01-01284/14/2020

Recently, car-sharing services using ultra-compact mobilities have been attracting attention as a means of transportation for one or two passengers in urban areas. A platooning system consisting of a manned leader vehicle and unmanned follower vehicles can reduce vehicle distributors. We have proposed a platooning system which controls vehicle motion based on the relative position and posture measured by non-contact coupling devices installed between vehicles. The feasibility of the coupling devices was validated through a HILS experiment. There are two basic requirements for realizing our platooning system; (1) all devices must remain coupled and (2) follower vehicles must be able to track the leader vehicle trajectory. Thus, this paper proposes two vehicle control method for satisfying those requirements. They are the “device coupling and trajectory tracking merging method” and the “trajectory shifting method”. The device coupling and trajectory tracking merging method consisting of a coupling keeping controller and a trajectory tracking controller. The predominant controller is chosen according to the amount of the coupling device error and the trajectory tracking error. The trajectory shifting method shifts the tracking target trajectory to keep the device coupled. The shifting amount is decided by the estimated turning radius of the leader vehicle. Platooning experiments using two 1:16 scale-model vehicles has been performed on the experiment course containing a straight section and a circular section. Experiment results revealed that the device coupling and trajectory tracking merging method can maintain the coupling of the device while limiting the trajectory tracking error to a certain range. Though the trajectory shifting method can reduce the coupling device error, it fails on both device coupling keeping and trajectory error limiting, owing to the inadequacy in estimating the turning radius of the leader.

Fukui, Rui, Ye, Qiwei, Suzuki, Ayumi, Warisawa, Shin’ichi

Effects of a Probability-Based Green Light Optimized Speed Advisory on Dilemma Zone Exposure

2020-01-01164/14/2020

Green Light Optimized Speed Advisory (GLOSA) systems have the objective of providing a recommended speed to arrive at a traffic signal during the green phase of the cycle. GLOSA has been shown to decrease travel time, fuel consumption, and carbon emissions; simultaneously, it has been demonstrated to increase driver and passenger comfort. Few studies have been conducted using historical cycle-by-cycle phase probabilities to assess the performance of a speed advisory capable of recommending a speed for various traffic signal operating modes (fixed-time, semi-actuated, and fully-actuated). In this study, a GLOSA system based on phase probability is proposed. The probability is calculated prior to each trip from a previous week’s, same time-of-day (TOD) and day-of-week (DOW) period, traffic signal controller high-resolution event data. By utilizing this advisory method, real-time communications from the vehicle to infrastructure (V2I) become unnecessary, eliminating data-loss related issues. The effects of three different advice approaches (conservative, balanced, and aggressive) on dilemma zone exposure are analyzed. Proof of concept is carried out by simulating drives through a test-route composed of an arterial that had historical high-resolution traffic signal event logs for a series of actuated-coordinated traffic signals during different TOD and DOW. A comparison was performed between unadvised and GLOSA advised trips obtained from approximately 486,000 simulated trajectories. Results were obtained by analyzing the vehicle’s probability of stopping from utilizing Traffic Engineering dilemma zone theory. Reductions of 93% in the amount of hard brakings and 96% in the number of crossings through red light were observed with the proposed system. This data suggests the feasibility of a probability-based advisory, as well as the viability of utilizing the proposed GLOSA system to minimize dilemma zone exposure.

Saldivar-Carranza, Enrique, Li, Howell, Kim, Woosung, Mathew, Jijo, Bullock, Darcy, Sturdevant, James

Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads

2020-01-08894/14/2020

This paper presents a hierarchical control architecture to coordinate a group of connected vehicles on signalized intersection roads, where vehicles are allowed to change lane to follow a prescribed path. The proposed hierarchical control strategy consists of two control levels: a high level controller at the intersection and a decentralized low level controller in each car. In the hierarchical control architecture, the centralized intersection controller estimates the target velocity for each approaching connected vehicle to avoid red light stop based on the signal phase and timing (SPAT) information. Each connected vehicle as a decentralized controller utilizes model predictive control (MPC) to track the target velocity in a fuel efficient manner. The main objective in this paper is to consider mandatory lane changes. As in the realistic scenarios, vehicles are not required to drive in single lane. More specifically, they more likely change their lanes prior to signals. Hence, the vehicle decentralized controllers must prepare to cooperate with the vehicle that has a mandatory lane change request (host vehicle). The cooperative mandatory lane change is accomplished by inserting a virtual vehicle on the host vehicle’s target lane. The simulation results show the advantage of our proposed approach on both the lane change duration and vehicle fuel economy.

Du, Zhiyuan, Xu, Bin, Pisu, Pierluigi

Cyberattacks and Countermeasures for Intelligent and Connected Vehicles

07-12-01-000510/14/2019

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 Vehicles

12-02-03-001310/14/2019

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

Cooperative Ramp Merging System: Agent-Based Modeling and Simulation Using Game Engine

12-02-02-00085/16/2019

Agent-based modeling and simulation (ABMS) has been a popular approach for modeling autonomous and interacting agents in a multi-agent system. Specifically, ABMS can be applied to connected and automated vehicles (CAVs) since CAVs can operate autonomously with the help of onboard sensors, and cooperate with each other through vehicle-to-everything (V2X) communications. In order to improve energy efficiency and mobility of traffic, we have developed an online feedforward/feedback longitudinal controller for CAVs to cooperatively merge at ramps. Agent-based CAV models were built in the Unity3D environment, where vehicles are given connectivity and autonomy through C#-based scripting application programming interface (API). Agent-based infrastructure model is also built as a Unity3D simulation network based on the city of Mountain View, California. A simulation of cooperative on-ramp merging is carried out with a distributed consensus-based protocol, and then compared with the human-in-the-loop simulation where the on-ramp merging vehicle is driven by four different human drivers on a driving simulator. The benefits of introducing the proposed protocol are evaluated in terms of travel time, energy consumption, and pollutant emissions. The results show that the proposed cooperative on-ramp merging protocol can reduce average travel time, energy consumption, and pollutant emissions by 7%, 8%, and 58%, respectively, when compared to the human-in-the-loop scenario.

Wang, Ziran, Wu, Guoyuan, Boriboonsomsin, Kanok, Barth, Matthew J., Han, Kyungtae, Kim, BaekGyu, Tiwari, Prashant

Energy-Efficient Cooperative Adaptive Cruise Control with Receding Horizon of Traffic, Route Topology, and Traffic Light Information

12-02-02-00065/16/2019

Advanced and cooperative vehicle (semi-) autonomous driving systems will become a necessity in the future for sustainable, convenient, and safe mobility. By utilizing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, a vehicle’s energy consumption can be reduced while maintaining safety and driving comfort. A holistic control strategy is presented, which in a novel way incorporates traffic lights, road speed limits, gradients, and curvature, as well as surrounding traffic and detailed powertrain characteristics into a single Model Predictive Control formulation. The performance of the system is evaluated using a realistic co-simulation toolchain representing the vehicle, driver, and road, including complex traffic conditions. The approach is valid for a wide range of scenarios, ranging from urban city driving to highways. Simulation results for a D-class passenger car with a diesel engine and an automatic transmission in an urban route show energy savings between 5% and 30% with an unchanged travel time, compared to a simulated human driver.

Wikström, Niklas, Parrilla, Alejandro Ferreira, Jones, Stephen John, Grauers, Anders

Trust-Based Control and Scheduling for UGV Platoon under Cyber Attacks

2019-01-10774/2/2019

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors. To deal with inaccurate information due to a V2C cyber attack, a RoboTrust algorithm is designed to analyze vehicle trustworthiness and eliminate information with low credit. Finally, a human operator scheduling algorithm is proposed when the number of abnormal UGVs exceeds the limit of what human operators can handle concurrently. Representative simulation results demonstrate that the proposed automated decision aid can effectively guide human operators when working with platoons under cyber attacks. The platoon survivability has been improved by the proposed algorithm when compared to those that operate without this system.

Li, Fangjian, Mikulski, Dariusz, Wagner, John R., Wang, Yue

An Immersive Vehicle-in-the-Loop VR Platform for Evaluating Human-to-Autonomous Vehicle Interactions

2019-01-01434/2/2019

The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.

Rastogi, Vibhor, Merco, Roberto, Kaur, Manveen, Rayamajhi, Anjan, Gavelli, Marco, Papa, Gianluca, Pisu, Pierluigi, Babu, Sabarish, Robb, Andrew, Martin, Jim

Validating Heavy-Duty Vehicle Models Using a Platooning Scenario

2019-01-12484/2/2019

Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. Aerodynamic drag can also be reduced by close-gap platooning using information from vehicle-to-vehicle communications. In order to achieve these goals, the designers of control strategies need to simulate a wide range of driving situations in which vehicles interact with other vehicles and the infrastructure in a closed-loop fashion. RoadRunner is a new model-based system engineering platform based on Autonomie software, which can collectively provide the necessary tools to predict energy consumption for various driving decisions and scenarios such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithms. In the first part of this paper, control algorithms for adaptive cruise control and cooperative adaptive cruise control inspired by the literature are implemented into RoadRunner, for vehicle model simulations of longitudinal movements in the environment considering real route information. In the second part of the paper, we present the validation of three heavy-duty truck models under a platooning scenario on a freeway, based on test data provided by Lawrence Berkeley Laboratory. RoadRunner builds the Matlab/Simulink diagram of the scenario, including the information flows between truck vehicle models. After the simulation, the results showed that discrepancies in average inter-vehicle gap were within 4% compared to test data, while many of the operational signals, including the fuel consumption, were well matched.

Kim, Namdoo, Karbowski, Dominik, Rousseau, Aymeric

Localization and Perception for Control and Decision-Making of a Low-Speed Autonomous Shuttle in a Campus Pilot Deployment

12-01-02-000311/12/2018

Future SAE Level 4 and Level 5 autonomous vehicles (AV) will require novel applications of localization, perception, control, and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This article concentrates on low-speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of the campus as an initial AV pilot test route for the deployment of low-speed autonomous shuttles. This article presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment. The article treats autonomous driving with Real-Time Kinematic (RTK) GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost, and hence lower accuracy GPS is desirable. Our in-house automated low-speed electric vehicle is used in experimental evaluation and verification. In addition, the experimental vehicle has vehicle to everything (V2X) communication capability and utilizes a dedicated short-range communication (DSRC) modem. It is able to communicate with instrumented traffic lights and with pedestrians and bicyclists with DSRC-enabled smartphones. Before real-world experiments, our connected and automated driving hardware-in-the-loop (HiL) simulator with real DSRC modems is used for extensive testing of the algorithms and the low-level longitudinal and lateral controllers. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. Model-in-the-loop simulation, HiL simulation, and experimental testing are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low-speed autonomous shuttle localization and perception algorithms for control and decision-making.

Wen, Bowen, Gelbal, Sukru Yaren, Guvenc, Bilin Aksun, Guvenc, Levent

Cybersecurity for Commercial Vehicles

R-4648/28/2018

This book provides a thorough view of cybersecurity to encourage those in the commercial vehicle industry to be fully aware and concerned that their fleet and cargo could be at risk to a cyber-attack. It delivers details on key subject areas including: • SAE International Standard J3061; the cybersecurity guidebook for cyber-physical vehicle systems • The differences between automotive and commercial vehicle cybersecurity. • Forensics for identifying breaches in cybersecurity. • Platooning and fleet implications. • Impacts and importance of secure systems for today and for the future. Cybersecurity for all segments of the commercial vehicle industry requires comprehensive solutions to secure networked vehicles and the transportation infrastructure. It clearly demonstrates the likelihood that an attack can happen, the impacts that would occur, and the need to continue to address those possibilities. This multi-authored presentation by subject-matter experts provides an interesting and dynamic story of how industry is developing solutions that address the critical security issues; the key social, policy, and privacy perspectives; as well as the integrated efforts of industry, academia, and government to shape the current knowledge and future cybersecurity for the commercial vehicle industry.

D'Anna, Gloria

Swarm Intelligence Based Algorithm for Management of Autonomous Vehicles on Arterials

2018-01-16468/7/2018

Connected and autonomous vehicles are different from traditional vehicles. The communication between vehicles (V2V) or between vehicles and infrastructures (V2I) renders it possible to convey traffic information (e.g. signal timing or speed advisory) from signal controllers to vehicles as well as vehicles to vehicles in real time. Taking this advantage, this paper aims to developing an algorithm which enables the interconnected autonomous vehicles running efficiently on arterials. A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating. The result of simulation shows that this swarm algorithm enables an autonomous vehicle to meet its own requirements quickly and form a stable platoon within 30 seconds. Due to the consistency of the individuals in a platoon, autonomous vehicle can maintain the small car-following gap. This decreases the fragmentation of road, thereby greatly improves the formation of platoons compared to individuals under high density circumstances. Moreover, it was found that the proposed swarm intelligence based algorithm increases the accessibility of arterial significantly.

Li, Lin, Hao, Ruochen, Ma, Wanjing, Qi, Xinzhou, Diao, Chenxue

A Modeling Framework for Connectivity and Automation Co-simulation

2018-01-06074/3/2018

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion. The framework, called RoadRunner, extends the capability of Autonomie, a vehicle energy consumption and performance modeling platform, to simulate the longitudinal movements of one or more user-defined vehicles along a user-defined route. In the first part of the paper, we provide an overview of how the framework is organized. The route attributes (position of traffic lights, grade, etc.) can be automatically extracted from a digital map after origin and destination are provided. The user defines which vehicle models to simulate and in which order. The Simulink model is then automatically generated from the scenario description. In the second part of the paper, we present an example case of a scenario with an eco-approach, using traffic signals that provide their signal phase and timing information to the vehicle. A two-stage control algorithm inspired by the literature is implemented to adjust the vehicle’s velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption. Finally, we present simulation results for speed patterns and powertrain operations in typical roads with multiple intersection, comparing the energy savings with traditional driving of unconnected vehicles.

Kim, Namdoo, Karbowski, Dominik, Rousseau, Aymeric

2021-12-29.TEST Visual Sensor Fusion and Data Sharing across Connected Vehicles for Active Safety

2018-01-0026

4/3/2018

The development of connected-vehicle technology, which includes vehicle-vehicle and vehicle-infrastructure 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 sensor data. In this paper, 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 achieve high accuracy, low delay and improved safety.

Al-Qassab, Hothaifa, Pang, Su, Al-Qizwini, Mohammed, Radha, Hayder