This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Theory of Collision Avoidance Capability in Automated Driving Technologies
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This paper proposes a theory to analyze the collision avoidance capability of automated driving technologies. The theory gives answers to a fundamental question whether automated vehicles fall into extreme conditions at all rather than another question how a vehicle reacts under extreme conditions (is it as safe as driver?). The theory clarifies the following matters: There are two types of hazards to cause collisions, cognitive hazards and behavioral hazards. Cognitive hazards are handled by controlling the upper limit speed of the automated vehicle including when stopped. There are two methods for handling behavioral hazards, preparation and response. The response known well is the coping method activated when the hazard is detected in the dynamic (operational) level. The preparation is the coping method operating at all time in the semantic (tactical) level. The collision condition in the semantic level is as follows, a collision occurs when the paths of two vehicles have a crossing point and the two vehicles drive on the crossing point at same time. The condition can be formulated as collision avoidance equation. Solving the equation means that the automated vehicle has prepared for the behavioral hazard before the hazard occurs. It is concluded that a collision avoidance capability consists of not only a response capability that supports the accuracy of collision avoidance in extreme conditions in the dynamic level but also a preparation capability that supports the accuracy to avoid reaching those extreme conditions in the semantic level. The preparation capability can be evaluated through stability analysis of the automated vehicle behavior given by the temporal backward simulation from each extreme condition. A remaining problem is how determine the upper limit of the hazards growing speed to which the automated vehicles should react.
CitationKINDO, T. and Okumura, B., "Theory of Collision Avoidance Capability in Automated Driving Technologies," SAE Technical Paper 2018-01-0044, 2018, https://doi.org/10.4271/2018-01-0044.
Data Sets - Support Documents
|Unnamed Dataset 1|
|Unnamed Dataset 2|
- Lemelson , J. and Pedersen , R. 1999
- Coelingh , E. , Eidehall , A. , and Bengtsson , M. Collision Warning with Full Auto Brake and Pedestrian Detection-a Practical Example of Automatic Emergency Braking Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on 155 160 2010
- Shimizu , T. and Raksincharoensak , P. Motion Planning via Optimization of Risk Quantified by Collision Velocity Accompanied with AEB Activation Vehicular Electronics and Safety (ICVES), 2017 IEEE International Conference on 19 25 2017
- Guo , C. , Kidono , K. , Machida , T. , Terashima , R. et al. Human-Like Behavior Generation for Intelligent Vehicles in Urban Environment Based on a Hybrid Potential Map 2017 IEEE Intelligent Vehicles Symposium (IV) 197 203 2017
- Montemerlo , M. , Becker , J. , Bhat , S. et al. Junior: The Stanford Entry in the Urban Challenge Journal of Field Robotics 25 9 569 597 2008
- Urmson , C. , Anhalt , J. , Bagnell , D. et al. Autonomous Driving in Urban Environments: Boss and the Urban Challenge Journal of Field Robotics 25 8 425 466 2008
- Okumura , B. , James , M. , Kanzawa , Y. et al. Challenges in Perception and Decision Making for Intelligent Automotive Vehicles: A Case Study IEEE Transactions on Intelligent Vehicles 1 1 20 32 2016
- Galceran , E. , Cunningham , A. , Eustice , R. , et al. Multipolicy Decision-Making for Autonomous Driving via Changepoint-Based Behavior Prediction Robotics: Science and Systems 2015
- The U.S. Department of Transportation https://www.transportation.gov/AV/federalautomated-vehicles-policy-september-2016 2016
- OICA 2017
- Nowakowski , C. , Shladover , S. , Chan , C. et al. Development of California Regulations to Govern the Testing and Operation of Automated Driving Systems California PATH Program 2014 http://docs.trb.org/prp/15-2269.pdf
- Dadras , S. , Gerdes , R. , and Sharma , R. Vehicular Platooning in an Adversarial Environment Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security 167 178 2015