Head-on Collision Avoidance Path Planning with Model Predictive Control

Document Type : Research Article

Authors

Faculty of Mechanical Engineering, K.N.Toosi University of Technology, Tehran, Iran

Abstract

Due to the high fatalities of head-on accidents, the design of intelligent systems to prevent such severe collisions is essential. In this study, path planning for head-on collision avoidance with a deviated vehicle from the opposite lane has been investigated. The main approach is based on a model predictive controller with 2 seconds of prediction horizon and a linearized prediction model with low errors near the operational conditions. A conservative method is used for lateral motion prediction of the deviated vehicle and based on that, the collision avoidance constraints of the model predictive planner are simply modeled by a new approach. Moreover, a novel method to choose the proper swerve direction of evasive maneuver is proposed. This method is based on keeping the ego vehicle away from dangerous directions and has different criteria for far and close encounters. The final algorithm is capable to control the steering of the prediction model with a constrained lateral acceleration and calculates safe and maneuverable paths for the aforementioned scenario. Four simulations are conducted to validate the algorithm in far and close encountering, and critical conditions of choosing swerve direction. Results show the robustness of the path planner, even to sudden deviations at close distances and with high lateral accelerations.

Keywords


[1] Critical reasons for crashes investigated in the national motor vehicle crash causation survey, National Highway Traffic Safety Administration (NHTSA), (2018).
[2] R. Lattarulo, J.P. Rastelli, A hybrid planning approach based on MPC and parametric curves for overtaking maneuvers, Sensors, 21(2) (2021) 1-19.
[3] B. Shahian-Jahromi, S.A. Hussain, B. Karakas, S. Cetin, Control of autonomous ground vehicles: a brief technical review, 4th International Conference on Mechanics and Mechatronics Research, (2017) 1-6.
[4] Table 29: Crashes by first harmful event, type of collision and crash severity, National Highway Traffic Safety Administration (NHTSA), (2016-2019).
[5] J. Chen, W. Zhan, M. Tomizuka, Constrained iterative LQR for on-road autonomous driving motion planning, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), (2017) 2232-2238.
[6] C. Katrakazas, M. Quddus, W.H. Chen,  L. Deka, Real-time motion planning methods for autonomous on-road driving: state of the art and future research directions, Transportation Research Part C: Emerging Technologies, 60 (2015) 416-442.
[7] S. Dixit, S. Fallah, U. Montanaro, M. Dianati, A. Stevens, F. Mccullough, A. Mouzakitis, Trajectory planning & tracking for autonomous overtaking: state of the art and future prospects, Annual Reviews in Control, 45 (2018) 76-86.
[8] B. Paden, M. Cap, S.Z. Yong, D. Yershov, E. Frazzoli, A survey of motion planning and control techniques for self-driving urban vehicles, IEEE Transactions on Intelligent Vehicles, 1 (2016) 33-55.
[9] Y. Rasekhipour, A. Khajepour, S.K. Chen, B. Litkouhi, A potential field-based model predictive path-planning controller for autonomous road vehicles, IEEE Transactions on Intelligent Transportation Systems, 18(5) (2016) 1255-1267.
[10] U.Z. AbdulHamid, H. Zamzuri, T. Yamada, M.A. AbdulRahman, Y. Saito, P. Raksincharoensak, Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 232(10) (2018) 1-21.
[11] J. Funke, P. Theodosis, R. Hindiyeh, G. Stanek, K. Kritatakirana, C. Gerdes, D. Langer, M. Hernandez, B.M. Bessler, B. Huhnke, Up to the limits: autonomous Audi TTS, Proceedings of the IEEE Intelligent Vehicles Symposium (IV), (2012) 541-547.
[12] W. Xu, J. Wei, J.M. Dolan, H. Zhao, H. Zha, A real-time motion planner with trajectory optimization for autonomous vehicles, Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), (2012) 2061-2067.
[13] D. Gonzalez, J. Perez, R. Lattarulo, V. Milanes, F. Nashashibi, Continuous curvature planning with obstacle avoidance capabilities in urban scenarios, Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (ITSC), (2014) 1430-1435.
[14] T. Berglund, A. Brodnik, H. Jonsson, M. Staffanson, Planning smooth and obstacle-avoiding B-spline paths for autonomous mining vehicles, IEEE Transactions on Automation Science & Engineering, 7 (2010) 167-172.
[15] A. Ghaffari, S.N. Minaee, Lane change path planning in emergency situation based on skilled driver's performance, Amirkabir Journal of Mechanical Engineering, 54(1) (2021), (in Persian).
[16] Y. Kuwata, G.A. Fiore, J. Teo, E. Frazzoli, J.P. How, Motion planning for urban driving using RRT, Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2008) 1681-1686.
[17] S. Karaman, E. Frazzoli, Incremental sampling-based algorithms for optimal motion planning, Robotics: Science and Systems (RSS 2010), (2010) 71-87.
[18] J. Ziegler, C. Stiller, Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios, Proceedings of the 2009 IEEE International Conference on Intelligent Robots and Systems (IROS), (2009) 1879-1884.
[19] M. McNaughton, C. Urmson, J.M. Dolan, J.W. Lee, Motion planning for autonomous driving with a conformal spatiotemporal lattice, IEEE International Conference on Robotics and Automation (ICRA), (2011) 4889-4895.
[20] J. Bohren, T. Foote, J. Keller, A. Kushleyev, D. Lee, A. Stewart, P. Vernaza, J. Derenick, J. Spletzer, B. Satterfield, Little Ben: The Ben Franklin racing team’s entry in the 2007 Darpa urban challenge, Journal of Field Robotics, 25(9) (2008) 598-614.
[21] D. Dolgov, S. Thrun, M. Montemerlo, J. Diebel, Practical search techniques in path planning for autonomous driving, Proceedings of the First International Symposium on Search Techniques in Artificial Intelligence and Robotics (STAIR-08), (2008).
[22] D. Ferguson, S. Anthony, Field D*: an interpolation-based path planner and replanner, Robotics Research, Springer Berlin Heidelberg, (2007) 239-253.
[23] G. Tianyu, J. Atwood, C. Dong, J.M. Dolan, J.W. Lee, Tunable and stable real-time trajectory planning for urban autonomous driving, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2015).
[24] G. Tianyu, J.M. Dolan, J.W. Lee, Runtime-bounded tunable motion planning for autonomous driving, IEEE Intelligent Vehicles Symposium (IV), (2016).
[25] C. Liu, W. Zhan, M. Tomizuka, Speed profile planning in dynamic environments via temporal optimization, IEEE Intelligent Vehicles Symposium (IV), (2017).
[26] J. Ziegler, P. Bender, T. Dang, C. Stiller, Trajectory planning for Bertha - a local, continuous method, Proceedings of the IEEE Intelligent Vehicle Symposium (IV), (2014) 450-457.
[27] X. Zhang, A. Liniger, F. Borrelli, Optimization-based collision avoidance, IEEE Transactions on Control Systems Technology, 29(3) (2020) 972-983.
[28] R. Patel, J. Goulart, Trajectory generation for aircraft avoidance maneuvers using online optimization, Journal of Guidance, Control and Dynamics, 34(1) (2011) 218-230.
[29] B. Kim, D. Kim, S. Park, Y. Jung, K. Yi, Automated complex urban driving based on enhanced environment representation with gps/map, radar, lidar and vision, IFAC-PapersOnLine, 49 (2016) 190-195.
[30] J. Nilsson, P. Falcone, M. Ali, J. Sjoberg, Receding horizon maneuver generation for automated highway driving, Control Engineering Practice, 41 (2015) 124-133.
[31] M.M. Jalalmaab, Model Predictive Control of Highway Emergency Maneuvering and Collision Avoidance, PhD Thesis, University of Waterloo, Ontario, Canada, (2017).
[32] M. Ammour, R. Orjuela, M. Basset, Collision avoidance for autonomous vehicle using MPC and time varying Sigmoid safety constraints, IFAC-PapersOnLine, 54(10) (2021) 39-44.
[33] Y. Gao, Model Predictive Control for Autonomous and Semiautonomous Vehicles, PhD Thesis, University of California, Berkeley, (2014).
[34] T. Idman, Path Planning and Trajectory Generation – Model Predictive Control, PhD Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, (2019).
[35] S. Dixit, U. Montanaro, M. Dianati, D. Oxtoby, T. Mizutani, A. Mouzakitis, S. Fallah, Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC, IEEE Transactions on Intelligent Transportation Systems, 21(6) (2020) 2310-2323.
[36] Q. Shi, J. Zhao, A.E. Kamel, I. Lopez-Juarez, MPC based vehicular trajectory planning in structured environment, IEEE Access, 9 (2021) 21998-22013.
[37] M. Obayashi, K. Uto, G. Takano, Appropriate overtaking motion generating method using predictive control with suitable car dynamics, IEEE 55th Conference on Decision and Control (CDC), (2016).
[38] M. Obayashi, G. Takano, Real-time autonomous car motion planning using NMPC with approximated problem considering traffic environment, International Federation of Automatic Control, (2018) 279-286.
[39] J. Kong, M. Pfeiffer, G. Schildbach, F. Borrelli, Kinematic and dynamic vehicle models for autonomous driving control design, Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), (2015) 1094-1099.
[40] H. Chen, X. Zhang, Path planning for intelligent vehicle collision avoidance of dynamic pedestrian using Att-Lstm, MSFM and MPC at un-signalized crosswalk, IEEE Transactions on Industrial Electronics, 69(4) (2021) 4285-4295.
[41] R. Hajiloo, M. Abroshan, A. Khajepour, A. Kasaiezadeh, S.K. Chen, Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems, 22(5) (2020) 3167-3178.
[42] M. Werling, D. Liccardo, Automatic collision avoidance using model-predictive online optimization, 51st IEEE Conference on Decision and Control, (2012).
[43] Z. Zuo, X. Yang, Z. Li, Y. Wang, Q. Han, L. Wang, MPC-based cooperative control strategy of path planning and trajectory tracking for intelligent vehicles, IEEE Transactions on Intelligent Vehicles, 6(3) (2021) 513-522.
[44] J. Palatti, A. Aksjonov, G.Alcan, V.Kyrki, Planning for safe abortable overtaking maneuvers in autonomous driving, 2021 IEEE International Intelligent Transportation Systems Conference, (2021).
[45] S. Lefevre, D. Vasquez, C. Laugier, A survey on motion prediction and risk assessment for intelligent vehicles, ROBOMECH Journal, Springer, 1(1) (2014) 1-14.
[46] M. Althoff, J.M. Dolan, Online verification of automated road vehicles using reachability analysis, IEEE Transactions on Robotics, 30 (2014) 903-918.
[47] R. Soloperto, J. Kohler, M.A. Muller, F. Allgower, Collision avoidance for uncertain nonlinear systems with moving obstacles using robust model predictive control, 18th European Control Conference (ECC), (2019).
[48] T. Kim, H.Y. Jeong, Crash probability and error rates for head-on collisions based on stochastic analyses, IEEE Transactions on Intelligent Transportation Systems, 11(4) (2010) 896-904.
[49] S. Kumari, S. Ghosh, D. Mitra, S. Sengupta, S. Mukhopadhyay, Collision risk assessment based on line of sight, IFAC-PapersOnLine, 53(2) (2020) 14972-14977.