Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning
Abstract
This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.
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M. S. Shirazi and B. T. Morris, “Looking at Intersections: A Survey of Intersection Monitoring, Behavior and Safety Analysis of Recent Studies,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 4–24, Jan. 2017, doi: 10.1109/TITS.2016.2568920.
I. Kamal, K. Housni, and M. Y. Hadi, “A survey of autonomous vehicles for traffic analysis,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 2, pp. 1016–1029, Feb. 2024, doi: 10.11591/IJEECS.V33.I2.PP1016-1029.
S. Baccari, M. Hadded, H. Ghazzai, H. Touati, and M. Elhadef, “Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges,” IEEE Access, vol. 12, pp. 19250–19276, 2024, doi: 10.1109/ACCESS.2024.3361829.
F. Sana, N. L. Azad, and K. Raahemifar, “Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review,” Machines, vol. 11, no. 7, 2023, doi: 10.3390/machines11070676.
K. Wang, G. Zhao, and J. Lu, “A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment,” IEEE Transactions on Intelligent Transportation Systems, 2024, doi: 10.1109/TITS.2024.3379993.
M. Reda, A. Onsy, A. Ghanbari, and A. Y. Haikal, “Path planning algorithms in the autonomous driving system: A comprehensive review,” Rob Auton Syst, vol. 174, 2024, doi: 10.1016/j.robot.2024.104630.
J. Medina-Lee, A. Artuñedo, J. Godoy, and J. Villagra, “Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios,” Sensors 2021, Vol. 21, Page 3755, vol. 21, no. 11, p. 3755, May 2021, doi: 10.3390/S21113755.
E. Ahmadi, A. Olama, R. C. Carlson, and E. Camponogara, “Signal-free Path-free Intersection Control for Connected Vehicles under Automated Driving,” IEEE Transactions on Intelligent Vehicles, 2024, doi: 10.1109/TIV.2024.3398556.
S. H. Nair, H. Lee, E. Joa, Y. Wang, H. E. Tseng, and F. Borrelli, “Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions,” Oct. 2023, Accessed: May 22, 2024. [Online]. Available: https://arxiv.org/abs/2310.20561v1
J. Li, L. Peng, S. Xu, and Z. Li, “Distributed edge signal control for cooperating pre-planned connected automated vehicle path and signal timing at edge computing-enabled intersections,” Expert Syst Appl, vol. 241, p. 122570, May 2024, doi: 10.1016/J.ESWA.2023.122570.
A. Jo, S. Kim, H. Lee, and K. Yi, “Ride Comfort-enhanced Optimal Decision and Planning Strategy at Urban Signalized Intersections Based on Hybrid Model Predictive Control,” IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 4866–4873, 2023, doi: 10.1109/ITSC57777.2023.10422387.
H. Liu, K. Chen, Y. Li, Z. Huang, J. Duan, and J. Ma, “Integrated Behavior Planning and Motion Control for Autonomous Vehicles with Traffic Rules Compliance,” 2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023, 2023, doi: 10.1109/ROBIO58561.2023.10354858.
S. Zhou, H. Xu, G. Zhang, T. Ma, and Y. Yang, “Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR,” J Intell Transp Syst, 2023, doi: 10.1080/15472450.2023.2209912.
K. Yu, L. Lin, M. Alazab, L. Tan, and B. Gu, “Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4337–4347, Jul. 2021, doi: 10.1109/TITS.2020.3042504.
S. Chen, X. Hu, J. Zhao, R. Wang, and M. Qiao, “A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments,” World Electric Vehicle Journal 2024, Vol. 15, Page 99, vol. 15, no. 3, p. 99, Mar. 2024, doi: 10.3390/WEVJ15030099.
J. Sun, X. Qi, Y. Xu, and Y. Tian, “Vehicle Turning Behavior Modeling at Conflicting Areas of Mixed-Flow Intersections Based on Deep Learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3674–3685, Sep. 2020, doi: 10.1109/TITS.2019.2931701.
R. Yao, W. Zeng, Y. Chen, and Z. He, “A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections,” Transp Res Part C Emerg Technol, vol. 132, p. 103415, Nov. 2021, doi: 10.1016/J.TRC.2021.103415.
B. Li, X. Chen, T. Acarman, X. Li, and Y. Zhang, “Recent Advances in Motion Planning and Control of Autonomous Vehicles,” Electronics 2023, Vol. 12, Page 4881, vol. 12, no. 23, p. 4881, Dec. 2023, doi: 10.3390/ELECTRONICS12234881.
Y. ; Chen, Y. Bian, Y. Chen, and Y. Bian, “Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties,” Electronics 2023, Vol. 12, Page 4248, vol. 12, no. 20, p. 4248, Oct. 2023, doi: 10.3390/ELECTRONICS12204248.
H. Sun, C. Zhang, C. Hu, and J. Zhang, “Event-triggered reconfigurable reinforcement learning motion-planning approach for mobile robot in unknown dynamic environments,” Eng Appl Artif Intell, vol. 123, p. 106197, Aug. 2023, doi: 10.1016/J.ENGAPPAI.2023.106197.
J. Hu, R. Chen, W. Xu, and R. Lu, “An event-triggered real-time motion planning strategy for autonomous vehicles,” https://doi.org/10.1177/09544062221098548, vol. 236, no. 19, pp. 10332–10348, May 2022, doi: 10.1177/09544062221098548.
L. Gurobi Optimization, “Gurobi Optimizer Reference Manual.” 2023. Accessed: May 08, 2024. [Online]. Available: https://www.gurobi.com/
T. Tran, J. Denny, and C. Ekenna, “Predicting Sample Collision with Neural Networks,” Jun. 2020, Accessed: Jun. 02, 2024. [Online]. Available: https://arxiv.org/abs/2006.16868v1
DOI: https://doi.org/10.17529/jre.v20i3.39281
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