Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
Source abstract
Abstract
Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle Dynamics and Control Systems · Traffic control and management · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratoire Drive, Université de Bourgogne, 58000 Nevers, France
Funding
French Ministry of Higher Education and Research
References (98)
Show source-provided references
1. Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G.P., and Cho, W. (2022). A review on autonomous vehicles: Progress, methods and challenges. Electronics, 11.10.3390/electronics11142162 ↗
2. Advancements, prospects, and impacts of automated driving systems10.1016/j.ijtst.2017.07.008 ↗
3. Autonomous driving: A bird’s eye view10.1049/iet-its.2018.5061 ↗
4. World Health Organization (2019, July 14). World Health Organization Road Traffic Injuries. 2018, Available online: https://www.who.int/newsroom/fact-sheets/detail/road-traffic-injuries.
5. Better transport accessibility, better health: A health economic impact assessment study for Melbourne, Australia10.1186/s12966-019-0853-y ↗
6. A constructive role for social science in the development of automated vehicles
7. Analytical review of standard Sae J3016 «taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles» with latest updates
8. Iberraken, D. (2020). Safe Trajectories and Sequential Bayesian Decision-Making Architecture for Reliable Autonomous Vehicle Navigation. [Ph.D. Thesis, Clermont Auvergne University].
9. General lane-changing model MOBIL for car-following models10.3141/1999-10 ↗
10. Arifin, B., Suprapto, B.Y., Sri Prasetyowati, A.D., and Nawawi, Z. (2019, January 2–3). The lateral control of autonomous vehicles: A review. Proceedings of the 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), Batam, Indonesia.10.1109/ICECOS47637.2019.8984590 ↗
11. Rajamani, R. (2011). Vehicle Dynamics and Control, Springer Science & Business Media.10.1007/978-1-4614-1433-9 ↗
12. Nested PID steering control for lane keeping in autonomous vehicles10.1016/j.conengprac.2011.08.005 ↗
13. Observer-based backstepping control method using reduced lateral dynamics for autonomous lane-keeping system10.1016/j.isatra.2018.09.016 ↗
14. Automatic steering control strategy for unmanned vehicles based on robust backstepping sliding mode control theory10.1109/ACCESS.2019.2917507 ↗
15. Liu, Q., Liu, Y., Liu, C., Chen, B., Zhang, W., Li, L., and Ji, X. (2018). Hierarchical lateral control scheme for autonomous vehicle with uneven time delays induced by vision sensors. Sensors, 18.10.3390/s18082544 ↗
16. A feedback linearization design for the control of vehicle’s lateral dynamics10.1007/s11071-007-9280-8 ↗
17. Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller
18. Tagne, G., Talj, R., and Charara, A. (2013, January 23–26). Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.10.1109/IVS.2013.6629545 ↗
19. A robust lateral tracking control strategy for autonomous driving vehicles10.1016/j.ymssp.2020.107238 ↗
20. Lateral control of autonomous vehicles based on fuzzy logic10.1016/j.conengprac.2014.09.015 ↗
21. Fuzzy observer-based transitional path-tracking control for autonomous vehicles10.1109/TITS.2020.2979431 ↗
22. Control of chaos in vehicle lateral motion using the sliding mode variable structure control10.1177/0954407017753529 ↗
23. Novel SMC control design for path following of autonomous vehicles with uncertainties and mismatched disturbances10.1007/s40435-018-0478-z ↗
24. Kebbati, Y., Ait-Oufroukh, N., Ichalal, D., and Vigneron, V. (Asian J. Control, 2022). Lateral control for autonomous wheeled vehicles: A technical review, Asian J. Control, in print.10.1002/asjc.2980 ↗
25. Review and performance evaluation of path tracking controllers of autonomous vehicles10.1049/itr2.12051 ↗
26. Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control
27. Unit sliding mode control for disturbed crowd dynamics system based on integral barrier Lyapunov function10.1109/ACCESS.2020.2994590 ↗
28. Barrier Lyapunov function-based sliding mode control for guaranteed tracking performance of robot manipulator10.1155/2013/978241 ↗
29. Robust control framework for lateral dynamics of autonomous vehicle using barrier Lyapunov function10.1109/ACCESS.2021.3068949 ↗
30. A robust, multiple control barrier function framework for input constrained systems10.1109/LCSYS.2021.3133418 ↗
31. He, S., Zeng, J., Zhang, B., and Sreenath, K. (2021, January 25–28). Rule-based safety-critical control design using control barrier functions with application to autonomous lane change. Proceedings of the 2021 American Control Conference (ACC), New Orleans, LA, USA.10.23919/ACC50511.2021.9482848 ↗
32. Dong, X., Pei, H., and Gan, M. (2021, January 15–17). Autonomous Vehicle Lateral Control Based on Fractional-order PID. Proceedings of the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Xi’an, China.10.1109/ITNEC52019.2021.9586818 ↗
33. Choi, W.Y., Kim, D.J., Kang, C.M., Lee, S.-H., and Chung, C.C. (2018, January 27–29). Autonomous Vehicle Lateral Maneuvering by Approximate Explicit Predictive Control. Proceedings of the 2018 Annual American Control Conference (ACC), Milwaukee, WI, USA.10.23919/ACC.2018.8431889 ↗
34. Li, Y., Chai, S., Chai, R., and Liu, X. (2020, January 27–29). An Improved Model Predictive Control Method for Vehicle Lateral Control. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.10.23919/CCC50068.2020.9188611 ↗
35. Lee, Y., and You, B. (2020, January 19–22). Comparison and Evaluation of Various Lateral Controller for Autonomous Vehicle. Proceedings of the 2020 International Conference on Electronics, Information, Communication (ICEIC), Barcelona, Spain.10.1109/ICEIC49074.2020.9051294 ↗
36. Lateral control of an autonomous vehicle10.1109/TIV.2018.2804173 ↗
37. A distributed model predictive control approach to active steering control of string stable cooperative vehicle platoon10.3182/20130904-4-JP-2042.00040 ↗
38. A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
39. Rausch, V., Hansen, A., Solowjow, E., Liu, C., Kreuzer, E., and Hedrick, J.K. (2017, January 24–26). Learning a deep neural net policy for end-to-end control of autonomous vehicles. Proceedings of the 2017 American Control Conference (ACC), Seattle, WA, USA.10.23919/ACC.2017.7963716 ↗
40. Sharma, S., Tewolde, G., and Kwon, J. (2018, January 3–5). Behavioral cloning for lateral motion control of autonomous vehicles using deep learning. Proceedings of the 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, USA.10.1109/EIT.2018.8500102 ↗
41. Lee, M.-J., and Ha, Y.-G. (2020, January 19–22). Autonomous driving control using end-to-end deep learning. Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Republic of Korea.10.1109/BigComp48618.2020.00-23 ↗
42. Maqueda, A.I., Loquercio, A., Gallego, G., García, N., and Scaramuzza, D. (2018, January 18–22). Event-based vision meets deep learning on steering prediction for self-driving cars. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00568 ↗
43. Chen, Y., Praveen, P., Priyantha, M., Muelling, K., and Dolan, J. (2019, January 7–11). Learning on-road visual control for self-driving vehicles with auxiliary tasks. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA.10.1109/WACV.2019.00041 ↗
44. Noise gradient strategy for an enhanced hybrid convolutional-recurrent deep network to control a self-driving vehicle10.1016/j.asoc.2020.106258 ↗
45. Deep imitation learning for autonomous vehicles based on convolutional neural networks10.1109/JAS.2019.1911825 ↗
46. Reinforcement learning and deep learning based lateral control for autonomous driving [application notes]10.1109/MCI.2019.2901089 ↗
47. Learning robust control policies for end-to-end autonomous driving from data-driven simulation10.1109/LRA.2020.2966414 ↗
48. Driving maneuvers prediction based autonomous driving control by deep monte carlo tree search10.1109/TVT.2020.2991584 ↗
49. Wang, P., Chan, C.-Y., and de La Fortelle, A. (2018, January 26–30). A reinforcement learning based approach for automated lane change maneuvers. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Suzhou, China.10.1109/IVS.2018.8500556 ↗
50. A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car10.1109/TNNLS.2019.2892327 ↗
51. Swain, S.K., Rath, J.J., and Veluvolu, K.C. (2021). Neural network based robust lateral control for an autonomous vehicle. Electronics, 10.10.3390/electronics10040510 ↗
52. Speed-adaptive model-free lateral control for automated cars10.1016/j.ifacol.2022.11.312 ↗
53. Extremum-Seeking-Based Adaptive Model-Free Control and Its Application to Automated Vehicle Path Tracking10.1109/TMECH.2022.3146727 ↗
54. Zhu, L. (2022, January 25–27). Analyze the Advantages and Disadvantages of Different Sensors for Autonomous Vehicles. Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022), Wuhan, China.10.2991/aebmr.k.220405.171 ↗
55. Reschka, A., Böhmer, J.R., Saust, F., Lichte, B., and Maurer, M. (2012, January 3–7). Safe, dynamic and comfortable longitudinal control for an autonomous vehicle. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Alcala de Henares, Spain.10.1109/IVS.2012.6232159 ↗
56. Low-speed longitudinal controllers for mass-produced cars: A comparative study
57. Design of longitudinal controller for automated driving bus
58. Longitudinal model identification and velocity control of an autonomous car
59. Robust H∞ design of an automotive cruise control system10.1016/j.ifacol.2015.10.049 ↗
60. Abdullahi, A., and Akkaya, S. (2020, January 8–10). Adaptive cruise control: A model reference adaptive control approach. Proceedings of the 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.10.1109/ICSTCC50638.2020.9259641 ↗
61. Raffin, A., Taragna, M., and Giorelli, M. (2017, January 3–5). Adaptive longitudinal control of an autonomous vehicle with an approximate knowledge of its parameters. Proceedings of the 2017 11th International Workshop on Robot Motion and Control (RoMoCo), Wąsowo Palace, Poland.10.1109/RoMoCo.2017.8003885 ↗
62. Longitudinal control of autonomous vehicles consisting power-train with non-linear characteristics
63. Longitudinal control for mengshi autonomous vehicle via cloud model10.1088/1757-899X/320/1/012018 ↗
64. Distributed receding horizon control of vehicle platoons: Stability and string stability10.1109/TAC.2011.2159651 ↗
65. Model predictive multi-objective vehicular adaptive cruise control10.1109/TCST.2010.2049203 ↗
66. Design and robustness analysis of autonomous vehicles in intersections10.1016/j.ifacol.2019.08.090 ↗
67. A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach
68. Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving10.1016/j.trc.2020.102662 ↗
69. Comparison of deep reinforcement learning and model predictive control for adaptive cruise control10.1109/TIV.2020.3012947 ↗
70. Peake, A., McCalmon, J., Raiford, B., Liu, T., and Alqahtani, S. (2020, January 9–11). Multi-agent reinforcement learning for cooperative adaptive cruise control. Proceedings of the 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), Baltimore, MD, USA.10.1109/ICTAI50040.2020.00013 ↗
71. Trajectory Prediction Using Graph-Based Deep Learning for Longitudinal Control of Autonomous Vehicles: A Proactive Approach for Autonomous Driving in Urban Dynamic Traffic Environments10.1109/MVT.2022.3207305 ↗
72. Velocity control in car-following behavior with autonomous vehicles using reinforcement learning10.1016/j.aap.2022.106729 ↗
73. Design and experimental validation of a cooperative driving system in the grand cooperative driving challenge10.1109/TITS.2012.2186513 ↗
74. LMI-based control of vehicle platoons for robust longitudinal guidance10.3182/20080706-5-KR-1001.02051 ↗
75. String-stable CACC design and experimental validation: A frequency-domain approach10.1109/TVT.2010.2076320 ↗
76. Kritayakirana, K., and Gerdes, J.C. (2009, January 12–14). Controlling an autonomous racing vehicle: Using feedforward and feedback to control steering and speed. Proceedings of the Dynamic Systems and Control Conference, Los Angeles, CA, USA.10.1115/DSCC2009-2572 ↗
77. Nonlinear cascade strategy for longitudinal control in automated vehicle guidance10.1016/j.conengprac.2014.02.003 ↗
78. Kianfar, R., Ali, M., Falcone, P., and Fredriksson, J. (2014, January 8–11). Combined longitudinal and lateral control design for string stable vehicle platooning within a designated lane. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.10.1109/ITSC.2014.6957819 ↗
79. Combined longitudinal and lateral control for automated vehicle guidance10.1080/00423114.2013.874563 ↗
80. Coupled longitudinal/lateral controllers for autonomous vehicles navigation, with experimental validation10.1016/j.conengprac.2019.05.001 ↗
81. Kebbati, Y., Ait-Oufroukh, N., Vigneron, V., and Ichalal, D. (2022, January 11–14). Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles. Proceedings of the 2022 European Control Conference (ECC), London, UK.10.23919/ECC55457.2022.9838192 ↗
82. A Lateral and Longitudinal Dynamics Control Framework of Autonomous Vehicles Based on Multi-Parameter Joint Estimation10.1109/TVT.2022.3163507 ↗
83. Latrech, C., Chaibet, A., Boukhnifer, M., and Glaser, S. (2018). Integrated Longitudinal and Lateral Networked Control System Design for Vehicle Platooning. Sensors, 18.10.3390/s18093085 ↗
84. Yang, Z., Zhang, Y., Yu, J., Cai, J., and Luo, J. (2018, January 20–24). End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.10.1109/ICPR.2018.8546189 ↗
85. Merging and diverging impact on mixed traffic of regular and autonomous vehicles10.1109/TITS.2020.2974291 ↗
86. Toromanoff, M., Wirbel, E., and Moutarde, F. (2020, January 13–19). End-to-end model-free reinforcement learning for urban driving using implicit affordances. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00718 ↗
87. Deep learning model predictive control for autonomous driving in unknown environments10.1016/j.ifacol.2018.11.593 ↗
88. A survey of deep learning techniques for autonomous driving10.1002/rob.21918 ↗
89. An adaptive modified neural lateral-longitudinal control system for path following of autonomous vehicles
90. Sharma, S., Tewolde, G., and Kwon, J. (2019, January 16–18). Lateral and longitudinal motion control of autonomous vehicles using deep learning. Proceedings of the 2019 IEEE International Conference on Electro Information Technology (EIT), Brookings, SD, USA.10.1109/EIT.2019.8833873 ↗
91. Chen, C., Seff, A., Kornhauser, A., and Xiao, J. (2015, January 7–13). Deepdriving: Learning affordance for direct perception in autonomous driving. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.312 ↗
92. Devineau, G., Polack, P., Altché, F., and Moutarde, F. (2018, January 4–7). Coupled longitudinal and lateral control of a vehicle using deep learning. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8570020 ↗
93. Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles10.1016/j.ymssp.2015.10.021 ↗
94. Liang, X., Wang, T., Yang, L., and Xing, E. (2018, January 8–14). Cirl: Controllable imitative reinforcement learning for vision-based self-driving. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_36 ↗
95. Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm10.1016/j.trc.2020.102649 ↗
96. Baheri, A., Kolmanovsky, I., Girard, A., Tseng, H.E., and Filev, D. (2020). Vision-based autonomous driving: A model learning approach. arXiv.10.23919/ACC45564.2020.9147510 ↗
97. Bouton, M., Nakhaei, A., Isele, D., Fujimura, K., and Kochenderfer, M.J. (2020, January 20–23). Reinforcement learning with iterative reasoning for merging in dense traffic. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.10.1109/ITSC45102.2020.9294338 ↗
98. Ye, F., Cheng, X., Wang, P., Chan, C.-Y., and Zhang, J. (November, January 9). Automated lane change strategy using proximal policy optimization-based deep reinforcement learning. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.10.1109/IV47402.2020.9304668 ↗