A Study on Longitudinal Motion Scenario Design for Verification of Advanced Driver Assistance Systems and Autonomous Driving Systems
Source abstract
Abstract
This paper proposes a test scenario design method that reflects the longitudinal characteristics of reality for effective verification of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). Since the target systems interact with the external environment differently from the existing vehicle control system, realistic and various verification scenarios are required for verification. The proposed method consists of a vehicle model for simulating the vehicle behavior and a driver model to actively respond to the driving environment. In particular, the driver model used a model predictive control (MPC) algorithm to reflect the characteristic of human drivers. The longitudinal driving characteristics of human drivers were derived through a large-scale driving database analysis and considered in the driver model. The proposed method was compared with an existing car-following model using computer simulations. It was confirmed that its longitudinal driving behavior is similar to that of human drivers and that various scenarios can be designed by changing the model parameters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Traffic control and management · Human-Automation Interaction and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
References (49)
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1. Pendleton, S.D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y.H., Rus, D., and Ang, M.H. (2017). Perception, Planning, Control, and Coordination for Autonomous Vehicles. Machines, 5.10.3390/machines5010006 ↗
2. Fényes, D., Németh, B., and Gáspár, P. (2021). A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles. Energies, 14.10.3390/en14020517 ↗
3. Nie, Z., and Farzaneh, H. (2020). Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm. Appl. Sci., 10.10.3390/app10155271 ↗
4. Wang, P., Gao, S., Li, L., Sun, B., and Cheng, S. (2019). Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm. Energies, 12.10.3390/en12122342 ↗
5. A Survey of Deep Learning Applications to Autonomous Vehicle Control10.1109/TITS.2019.2962338 ↗
6. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, January 13–15). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning; PMLR, Mountain View, CA, USA.
7. Deshmukh, J., and Ničković, D. (2020, January 6–9). Runtime Verification of Autonomous Driving Systems in CARLA. Proceedings of the Runtime Verification, Los Angeles, CA, USA.10.1007/978-3-030-60508-7 ↗
8. Arcidiacono, A. (2018). ADAS Virtual Validation: ACC and AEB Case Study with IPG CarMaker. [Master’s Thesis, Politecnico di Torino].
9. A Framework for Virtual Testing of ADAS10.4271/2016-01-0049 ↗
10. Park, C., Chung, S., and Lee, H. (2020). Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System. Appl. Sci., 10.10.3390/app10082645 ↗
11. Vehicle-in-the-Loop at Testbeds for ADAS/AD Validation10.1007/s38314-021-0639-2 ↗
12. Tettamanti, T., Szalai, M., Vass, S., and Tihanyi, V. (2018, January 12–14). Vehicle-in-the-Loop Test Environment for Autonomous Driving with Microscopic Traffic Simulation. Proceedings of the 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain.10.1109/ICVES.2018.8519486 ↗
13. Van Ratingen, M.R. (2017). The Euro Ncap Safety Rating. Karosseriebautage Hamburg 2017, Springer.10.1007/978-3-658-18107-9_2 ↗
14. ISO (2018). Intelligent Transport Systems–Adaptive Cruise Control Systems–Performance Requirements and Test Procedures, ISO.
15. Hulshof, W., Knight, I., Edwards, A., Avery, M., and Grover, C. (2013, January 27–30). Autonomous Emergency Braking Test Results. Proceedings of the 23rd International Technical Conference on the Enhanced Safety of Vehicles (ESV), Seoul, Republic of Korea.
16. Zhao, D. (2016). Accelerated Evaluation of Automated Vehicles. [Ph.D. Thesis, University of Michigan].
17. Peng, H., and Leblanc, D. (2022, December 16). Evaluation of the Performance and Safety of Automated Vehicles. White Paper on NSF Transportation CPS Work. Available online: https://cps-vo.org/sites/default/files/webform/Evaluation_of_the_Performance_and_Safety_of_Automated_Vehicles_0.pdf.
18. Lee, K. (2004). Longitudinal Driver Model and Collision Warning and Avoidance Algorithms Based on Human Driving Databases, University of Michigan.
19. Klischat, M., and Althoff, M. (2019, January 9–12). Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.10.1109/IVS.2019.8814230 ↗
20. Challenges in Autonomous Vehicle Testing and Validation10.4271/2016-01-0128 ↗
21. Survey on Scenario-Based Safety Assessment of Automated Vehicles10.1109/ACCESS.2020.2993730 ↗
22. Fellendorf, M., and Vortisch, P. (2010). Microscopic Traffic Flow Simulator VISSIM. Fundamentals of Traffic Simulation, Springer.10.1007/978-1-4419-6142-6_2 ↗
23. Kaths, J., and Krause, S. (2016, January 20–21). Integrated Simulation of Microscopic Traffic Flow and Vehicle Dynamics. Proceedings of the IPG Apply & Innovate 2016, Karlsruhe, Germany.
24. An Operational Analysis of Traffic Dynamics10.1063/1.1721265 ↗
25. Nonlinear Follow-the-Leader Models of Traffic Flow10.1287/opre.9.4.545 ↗
26. Nonlinear Effects in the Dynamics of Car Following10.1287/opre.9.2.209 ↗
27. The Characteristics of Model-Following Systems as Synthesized by Optimal Control10.1109/TAC.1964.1105757 ↗
28. A Behavioural Car-Following Model for Computer Simulation10.1016/0191-2615(81)90037-0 ↗
29. A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation10.3390/smartcities4010019 ↗
30. Dingus, T.A., Klauer, S.G., Neale, V.L., Vicki, L., Petersen, A., Lee, S.E., Sudweeks, J., Perez, M.A., Hankey, J., and Ramsey, D. (2006). The 100-Car Naturalistic Driving Study, Phase II Results of the 100-Car Field Experiment.10.1037/e624282011-001 ↗
31. Ervin, R., Sayer, J., LeBlanc, D., Bogard, S., Mefford, M., Hagan, M., Bareket, Z., and Winkler, C. (2005). Automotive Collision Avoidance System Field Operational Test Report: Methodology and Results, Transportation Research Board.
32. LeBlanc, D.J. (2006). Road Departure Crash Warning System Field Operational Test: Methodology and Results. Volume 1: Technical Report, Transportation Research Institute, University of Michigan.
33. Victor, T., Bärgman, J., Hjälmdahl, M., Kircher, K., Svanberg, E., Hurtig, S., Gellerman, H., and Moeschlin, F. (2010). Sweden-Michigan Naturalistic Field Operational Test (Semifot) Phase 1: Final Report, Chalmers.
34. Sayer, J., LeBlanc, D., Bogard, S., Funkhouser, D., Bao, S., Buonarosa, M.L., and Blankespoor, A. (2011). Integrated Vehicle-Based Safety Systems Field Operational Test: Final Program Report, Transportation Research Institute, University of Michigan.10.1037/e621772011-001 ↗
35. Safety Pilot Model Deployment: Test Conductor Team Report
36. (2022, October 20). Traffic Analysis Tools: Next Generation Simulation-FHWA Operations, Available online: https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.
37. (2022, October 20). Open Dataset. Available online: https://waymo.com/open/.
38. Krajewski, R., Bock, J., Kloeker, L., and Eckstein, L. (2018, January 4–7). The HighD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569552 ↗
39. Bifulco, G.N., Simonelli, F., and Di Pace, R. (2008, January 4–6). Experiments toward an Human-like Adaptive Cruise Control. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.10.1109/IVS.2008.4621213 ↗
40. Car-Following Behavior with Instantaneous Driver–Vehicle Reaction Delay: A Neural-Network-Based Methodology10.1016/j.trc.2013.09.010 ↗
41. A Simple Nonparametric Car-Following Model Driven by Field Data10.1016/j.trb.2015.07.010 ↗
42. Estimating Risk Effects of Driving Distraction: A Dynamic Errorable Car-Following Model10.1016/j.trc.2014.07.013 ↗
43. Rajamani, R. (2012). Vehicle Dynamics and Control, Springer.10.1007/978-1-4614-1433-9 ↗
44. Kiencke, U., and Nielsen, L. (2000). Automotive Control Systems: For Engine, Driveline, and Vehicle, IOP Publishing Ltd.
45. Lenzo, B. (2022). Automated Driving Vehicles. Vehicle Dynamics: Fundamentals and Ultimate Trends, Springer International Publishing. CISM International Centre for Mechanical Sciences.10.1007/978-3-030-75884-4 ↗
46. Borrelli, F., Bemporad, A., and Morari, M. (2017). Predictive Control for Linear and Hybrid Systems, Cambridge University Press.10.1017/9781139061759 ↗
47. On the Human Control of Vehicles: An Experimental Study of Acceleration10.1007/s12544-013-0120-2 ↗
48. Xia, L., Li, P., Su, Z., Chen, T., Deng, Z., and Sun, D. (2022). Longitudinal Driving Behavior before, during, and after a Left-Turn Movement at Signalized Intersections: A Naturalistic Driving Study in China. Sustainability, 14.10.3390/su141811630 ↗
49. Loader, C. (2006). Local Regression and Likelihood, Springer Science & Business Media.