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A Trajectory-Based Method for Scenario Analysis and Test Effort Reduction for Highly Automated Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 2, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Unlike the test of passive safety of traditional vehicles, highly automated vehicles (HAV) need more capabilities to be tested. Besides, there are more parameter combinations for the scenarios that need to be tested for each capability, resulting in a high time-consuming and costs for the autonomous vehicle tests. This paper proposes a method for scenario analysis and test effort reduction. Firstly, the trajectories of the vehicle under test (VUT) in the scenario are analyzed, and the trajectories which lead to the test mission failure are obtained. Based on the above trajectories, the threshold that lead to the test mission failure, or a combination of thresholds are analyzed. The above thresholds or a combination of thresholds values are defined as Scenario Character Parameter (SCP). The process of the analysis of the SCPs are related to the abilities of the HAV, but does not depend on the specific algorithm of the HAV. Therefore, through the above analysis of trajectories and SCPs, the ability of the scenario to measure the performance of HAVs can be quantized. After completing the analysis of scenarios that are used in HAVs evaluation, the SCPs corresponding to each scenario are obtained. The SCPs have the relationships such as overlapping or inclusive. Then, a set of scenarios with minimum number but still cover all SCPs can be searched. Use this set of scenarios to replace the original combination of test scenarios, the number of scenarios that need to be tested can be reduced. The method proposed in this paper reduces the amount of tests and costs for HAVs, which will be a promote to the development of the HAV technology.
CitationQi, Y., Luo, Y., Li, K., Kong, W. et al., "A Trajectory-Based Method for Scenario Analysis and Test Effort Reduction for Highly Automated Vehicle," SAE Technical Paper 2019-01-0139, 2019, https://doi.org/10.4271/2019-01-0139.
Data Sets - Support Documents
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- Bengler, K., Dietmayer, K., Farber, B., and Maurer, M. , “Three Decades of Driver Assistance Systems: Review and Future Perspectives,” IEEE Intelligent Transportation Systems Magazine 6(4):6-22, 2014.
- Melcher, V., Rauh, S., Diederichs, F., Widlroither, H. et al. , “Take-Over Requests for Automated Driving,” Procedia Manufacturing 3:2867-2873, 2015.
- Braunagel, C., Rosenstiel, W., and Kasneci, E. , “Ready for Take-over? A New Driver Assistance System for an Automated Classification of Driver Take-over Readiness [J],” IEEE Intelligent Transportation Systems Magazine 9(4):10-22, 2017.
- Waymo Safety Report: On the Road to Fully Self-Driving
- Fort Safety Report.
- Nuro Safety Report.
- General Motors Safety Report.
- Zhao, D., Huang, X., Peng, H., Lam, H. et al. , “Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers,” IEEE Transactions on Intelligent Transportation Systems 19(3):733-744, 2018.
- Nowakowski, C., Shladover, S.E., Chan, C.Y. et al. , “Development of California Regulations to Govern Testing and Operation of Automated Driving Systems [J],” Transportation Research Record Journal of the Transportation Research Board 2489(4):137-144, 2015.
- Nowakowski, C., Shladover, S.E., and Chan, C.Y. , “Determining the Readiness of Automated Driving Systems for Public Operation: Development of Behavioral Competency Requirements [J],” Transportation Research Record Journal of the Transportation Research Board 2559:65-72, 2016.
- Shladover, S. E. and Nowakowski, C. , “Regulatory Challenges for Road Vehicle Automation: Lessons from the California Experience,” 2017.
- Ulbrich, S., Menzel, T., Reschka, A., and Schuldt, F. , “Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving,” in IEEE, International Conference on Intelligent Transportation Systems, 2015, 982-988.
- Menzel, T., Bagschik, G., and Maurer, M. , “Scenarios for Development, Test and Validation of Automated Vehicles,” 2018.
- Wittmann, D., Lienkamp, M., and Wang, C. , “Method for Comprehensive and Adaptive Risk Analysis for the Development of Automated Driving. In Intelligent Transportation Systems (ITSC),” in 2017 IEEE 20th International Conference on, October, 2017, 1-7.
- Amersbach, C. and Winner, H. , “Functional Decomposition: An Approach to Reduce the Approval Effort for Highly Automated Driving,” 8. Tagung Fahrerassistenz, 2017.
- Bach, J., Langner, J., Otten, S., Sax, E. et al. , “Test Scenario Selection for System-Level Verification and Validation of Geolocation-Dependent Automotive Control Systems, in Engineering, Technology and Innovation (ICE/ITMC), 2017 International Conference on, June 2017, 203-210.
- Gruner, R., Henzler, P., Hinz, G., Eckstein, C. et al. , “Spatiotemporal Representation of Driving Scenarios and Classification Using Neural Networks,” in In Intelligent Vehicles Symposium (IV), June 2017, IEEE, 1782-1788.
- Rocklage, E. , “Teaching Self-Driving Cars to Dream: A Deeply Integrated, Innovative Approach for Solving the Autonomous Vehicle Validation Problem, in Intelligent Transportation Systems (ITSC) IEEE 20th International Conference on, October 2017, 1-7.
- Ziegler, J., and Stiller, C. , “Spatiotemporal State Lattices for Fast Trajectory Planning in Dynamic on-Road Driving Scenarios,” in Intelligent Robots and Systems, IROS 2009. IEEE/RSJ International Conference on, October 2009, 1879-1884.
- Hallerbach, S., Xia, Y., Eberle, U., and Koester, F. , “Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles,” SAE Intl. J CAV 1(2), 2018, doi:10.4271/2018-01-1066.
- Wachenfeld, W., Junietz, P., Wenzel, R., and Winner, H. , “The Worst-Time-to-Collision Metric for Situation Identification,” in Intelligent Vehicles Symposium (IV), 2016, IEEE, 729-734.
- ISO/PAS 21448:2019. “Road Vehicles - Safety of the Intended Functionality.”
- Likhachev, M. and Ferguson, D. , “Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles,” The International Journal of Robotics Research 28(8):933-945, 2009.
- Patz, B.J., Papelis, Y., Pillat, R., Stein, G. et al. , “A Practical Approach to Robotic Design for the Darpa Urban Challenge,” Journal of Field Robotics 25(8):528-566, 2008.
- González, D., Pérez, J., Milanés, V., and Nashashibi, F. , “A Review of Motion Planning Techniques for Automated Vehicles,” IEEE Trans. Intelligent Transportation Systems 17(4):1135-1145, 2016.
- Ziegler, J., and Christoph S. , "Spatiotemporal State Lattices for Fast Trajectory Planning in Dynamic on-Road Driving Scenarios." In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, 1879-1884.
- Pivtoraiko, M. and Kelly, A. , “Efficient Constrained Path Planning via Search in State Lattices," in International Symposium on Artificial Intelligence, Robotics, and Automation in Space, 2005, 1-7.