Validation of scenario-based virtual safety testing using low-cost sensor-based instrumented vehicle
DOI:
https://doi.org/10.15282/jmes.17.2.2023.10.0754Keywords:
Autonomous Vehicle, Low Cost Sensor, IPG CarMaker, Safety Testing, ScenariosAbstract
Autonomous vehicle (AV) requires millions of miles on road to test the reliability of safety systems. It is also difficult to test the AV for critical scenarios which are rare but will endanger road users. Therefore, virtual safety testing simulation platforms are introduced to test the safety systems of the autonomous vehicles in critical scenarios. However, developing the virtual safety testing simulation platform requires information about the environment and driving data from the real world. Besides, it is challenging to build a system to collect driving data which is normally cost intensive especially in developing countries. Paradoxically, these developing countries have poor traffic environment which can provide valuable scenarios for safety testing test cases. Therefore, in this paper, a scenario-based testing using virtual simulation platform is developed using data captured by a low-cost sensor-based instrumented vehicle. The instrumented vehicle is built by low-cost off-the-shelf components for the testing purpose. The instrumented vehicle is used for validation process in IPG CarMaker’s vehicle model using SAE standards. Then, the validated vehicle model is used as an autonomous vehicle in IPG CarMaker for the virtual scenario-based safety testing. The whole validation process from data collection to data logging is carried out using various economic sensors instead of a single industrial system. This approach greatly reduce the cost of the instrumented vehicle and the result of the scenario-based testing shows that the virtual scenarios developed in IPG CarMaker can be used for validation purpose with actual scenarios using low-cost sensor based instrumented vehicle as low as 4% root mean square percentage error.
References
S. A. Bagloee, M. Tavana, M. Asadi, and T. Oliver, “Autonomous vehicles: challenges, opportunities, and future implications for transportation policies,” Journal of Modern Transportation, vol. 24, no.4, pp. 284–303, 2016.
F. Rosique, P. J. Navarro, C. Fernández, and A. Padilla, “A systematic review of perception system and simulators for autonomous vehicles research,” Sensors (Switzerland), vol. 19, no. 3, pp. 1-29, 2019.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in 1stConference on Robot Learning, United State, 2017.
H. Fan et al., “Baidu apollo EM motion planner,” ArXiv[Online], 2018, Available: https://arxiv.org/pdf/1807.08048.pdf.
G. Rong et al., “LGSVL simulator: A high fidelity simulator for autonomous driving,” ArXiv[Online], 2020, Available: https://arxiv.org/pdf/2005.03778.pdf.
S. Riedmaier, T. Ponn, D. Ludwig, B. Schick, and F. Diermeyer, “Survey on scenario-based safety assessment of automated vehicles,” IEEE Access, vol. 8, pp. 87456–87477, 2020.
S. Riedmaier, D. Schneider, D. Watzenig, F. Diermeyer, and B. Schick, “Model validation and scenario selection for virtual-based homologation of automated vehicles,” Applied Sciences (Switzerland), vol. 11, no. 1, pp. 1–24, 2021.
J. D. Setiawan, M. Safarudin, and A. Singh, “Modeling, simulation and validation of 14 DOF full vehicle model,” in International Conference on Instrumentation,Communication, Information Technology and Biomedical Engineering, pp. 1-6, Bandung Indonesia, 2009.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,”International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
X. Huang et al., “The apolloscape dataset for autonomous driving,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018, pp. 1067–1073, 2018.
P. V. Gopi Krishna Rao, M. V. Subramanyam, and K. Satyaprasad, “Study on PID controller design and performance based on tuning techniques,” in International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT 2014), Kanyakumari, India, pp. 1411–1417, 2014.
H. Caesar etal., “nuScenes: A multimodal dataset for autonomous driving,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, USA, pp. 11618–11628, 2020.
J. Mao et al., “One million scenes for autonomous driving: Once dataset,” ArXiv[Online], 2021, Available: https://arxiv.org/pdf/2106.11037.pdf.
M. Sheeny, E. D. Pellegrin, S. Mukherjee, A. Ahrabian, S. Wang, and A. Wallace, “Radiate: A radar dataset for automotive perception in bad weather,” in IEEE International Conference on Robotics and Automation (ICRA2021), Xian, China, pp. 1-7, 2021.
K. Burnett et al., “Boreas: A multi-season autonomous driving dataset,” The International Journal of Robotics Research, vol. 42, pp. 33-42, 2023.
H.-H. Braess et al., "Vieweg Handbuch Kraftfahrzeugtechnik," Spinger Vieweg Wiesbaden, Germany, 2013.
M. Zhu, X. Wang, and Y. Wang, “Human-like autonomous car-following model with deep reinforcement learning,” Transportation Research Part C: Emerging Technologie., vol. 97, pp. 348–368, 2018.
I. Bae et al., “Self-driving like a human driver instead of a robocar: Personalized comfortable driving experience for autonomous vehicles,” in 33rd Conference on Neutral Information Processing System (NeurIPS 2019), Vancouver, Canada, 2019.
Y. Dong et al., “Mcity data collection for automated vehicles study,”ArXiv[Online], 2019, Available: https://arxiv.org/pdf/1912.06258.pdf.
J. Rehder and R. Siegwart, “Camera/IMU calibration revisited,” IEEE Sensors Journal, vol. 17, no. 11, pp. 3257–3268, 2017.
M. Moussa, A. Moussa, and N. El-Sheimy, “Steering angle assisted vehicular navigation using portable devices in GNSS-denied environments,” Sensors (Switzerland), vol. 19, no. 7, pp. 1-18, 2019.
L. Fridman, D. E. Brown, W. Angell, I. Abdić, B. Reimer, and H. Y. Noh, “Automated synchronization of driving data using vibration and steering events,” Pattern Recognition Letters, vol. 75, pp. 9–15, 2016.
W. Yao et al., “On-road vehicle trajectory collection and scene-based lane change analysis: Part II,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 206–220, 2017.
S. Feraco, A. Bonfitto, N. Amati, and A. Tonoli, “Redundant multi-object detection for autonomous vehicles in structured environments,” Electrical Engineering in Transport, vol. 24, no. 1, pp. 1–17, 2022.
A. Agnoor, P. Atmakuri, and R. Sivanandan, “Analysis of driving behaviour through instrumented vehicles,” in 14thInternational Conference on COMmunication Systems and NETworkS(COMSNETS 2022), Bangalore, India, pp. 700–706, 2022.
G. Li, Y. Yang, S. Li, X. Qu, N. Lyu, and S. E. Li, “Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness,” Transportation Research Part C: Emerging Technologies, vol. 134, p. 103452, 2022.
D. Rempe, J. Philion, L. J. Guibas, S. Fidler, and O. Litany, “Generating useful accident-prone driving scenarios via a learned traffic prior,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, USA, pp. 17284–17294, 2022.
J. Cheng, L. Zhang, Q. Chen, X. Hu, and J. Cai, “A review of visual SLAM methods for autonomous driving vehicles,” Engineering Applications of Artificial Intelligence, vol. 114, p. 104992, 2022.
A. Amini et al., “VISTA 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles,” in IEEE International Conference on Robotics and Automation (ICRA 2022), Philadelphia, USA, pp. 2419–2426, 2022.
Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue, and B. Zhou, “MetaDrive: Composing diverse driving scenarios for generalizable reinforcement learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3461–3475, 2023.
H. Yue, L. Zhang, H. Shan, H. Liu, and Y. Liu, “Estimation of the vehicle’s centre of gravity based on a braking model,” Vehicle System Dynamics, vol. 53, no. 10, pp. 1520–1533, 2015.
J. Lv, A. A. Ravankar, Y. Kobayashi, and T. Emaru, “A method of low-cost IMU calibration and alignment,” in IEEE/SICE International Symposium on System Integration (SII 2016), Sapporo, Japan, pp. 373–378, 2016.
M. Garrosa, E. Olmeda, S. F. Del Toro, and V. Díaz, “Holistic vehicle instrumentation for assessing driver driving styles,” Sensors, vol. 21, no. 4, pp. 1–28, 2021.
A. Parra, D. Cagigas, A. Zubizarreta, A. J. Rodriguez, and P. Prieto, “Modelling and validation of full vehicle model based on a novel multibody formulation,” in 45thAnnual Conference of the IEEE Industrial Electronis Society, Lisbon, Portugal, pp. 675–680, 2019.
C. J. Hong and V. R. Aparow, “System configuration of human-in-the-loop simulation for level 3 autonomous vehicle using IPG CarMaker,” inIEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS 2021), Bandung, Indonesia, pp. 215–221, 2021.
V. R. Aparow et al., “Scenario based simulation testing of autonomous vehicle using Malaysian road,” in 5thInternational Conference on Vision, Image and Signal Processing (ICVISP 2021), Kuala Lumpur, Malaysia, pp. 33–38, 2021.
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