Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks (2403.19633v1)

Published 28 Mar 2024 in eess.SY and cs.SY

Abstract: This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes Model Predictive Control (MPC) with Generative Adversarial Networks (GAN) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman Filter to mitigate sensor noise. Simulation studies are investigated in different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (45)
  1. V. L. Neale, T. A. Dingus, S. G. Klauer, J. Sudweeks, and M. Goodman, “An overview of the 100-car naturalistic study and findings,” National Highway Traffic Safety Administration, Paper, vol. 5, p. 0400, 2005.
  2. M. Peden, R. Scurfield, D. Sleet, D. Mohan, A. A. Hyder, E. Jarawan, C. D. Mathers, et al., “World report on road traffic injury prevention,” 2004.
  3. J. Guanetti, Y. Kim, and F. Borrelli, “Control of connected and automated vehicles: State of the art and future challenges,” Annual Reviews in Control, vol. 45, pp. 18–40, 2018.
  4. S. Bae, Y. Kim, Y. Choi, J. Guanetti, P. Gill, F. Borrelli, and S. J. Moura, “Ecological adaptive cruise control of plug-in hybrid electric vehicle with connected infrastructure and on-road experiments,” Journal of Dynamic Systems, Measurement, and Control, vol. 144, no. 1, p. 011109, 2022.
  5. J. Nilsson, J. Silvlin, M. Brannstrom, E. Coelingh, and J. Fredriksson, “If, when, and how to perform lane change maneuvers on highways,” IEEE Intelligent Transportation Systems Magazine, vol. 8, no. 4, pp. 68–78, 2016.
  6. D. J. Sun and L. Elefteriadou, “Lane-changing behavior on urban streets: A focus group-based study,” Applied ergonomics, vol. 42, no. 5, pp. 682–691, 2011.
  7. M. Rahman, M. Chowdhury, Y. Xie, and Y. He, “Review of microscopic lane-changing models and future research opportunities,” IEEE transactions on intelligent transportation systems, vol. 14, no. 4, pp. 1942–1956, 2013.
  8. G. Schildbach and F. Borrelli, “Scenario model predictive control for lane change assistance on highways,” in 2015 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2015, pp. 611–616.
  9. J. Chen, P. Zhao, T. Mei, and H. Liang, “Lane change path planning based on piecewise bezier curve for autonomous vehicle,” in Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety.   IEEE, 2013, pp. 17–22.
  10. A. Kelly and B. Nagy, “Reactive nonholonomic trajectory generation via parametric optimal control,” The International Journal of Robotics Research, vol. 22, no. 7-8, pp. 583–601, 2003.
  11. M. Bouton, A. Nakhaei, K. Fujimura, and M. J. Kochenderfer, “Cooperation-aware reinforcement learning for merging in dense traffic,” arXiv preprint arXiv:1906.11021, 2019.
  12. J. E. Naranjo, C. Gonzalez, R. Garcia, and T. De Pedro, “Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 438–450, 2008.
  13. F. You, R. Zhang, G. Lie, H. Wang, H. Wen, and J. Xu, “Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system,” Expert Systems with Applications, vol. 42, no. 14, pp. 5932–5946, 2015.
  14. D. Sadigh, S. Sastry, S. A. Seshia, and A. D. Dragan, “Planning for autonomous cars that leverage effects on human actions.” in Robotics: Science and Systems, vol. 2, 2016.
  15. Y. Hu, A. Nakhaei, M. Tomizuka, and K. Fujimura, “Interaction-aware decision making with adaptive strategies under merging scenarios,” in International Conference on Intelligent Robots and Systems.   IEEE/RSJ, 2019.
  16. D. Isele, “Interactive decision making for autonomous vehicles in dense traffic,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC).   IEEE, 2019, pp. 3981–3986.
  17. S. Bae, D. Saxena, A. Nakhaei, C. Choi, K. Fujimura, and S. Moura, “Cooperation-aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network,” in 2020 American Control Conference (ACC).   IEEE, 2020, pp. 1209–1216.
  18. D. M. Saxena, S. Bae, A. Nakhaei, K. Fujimura, and M. Likhachev, “Driving in dense traffic with model-free reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 5385–5392.
  19. K. Lee, D. Isele, E. Theodorou, and S. Bae, “Spatiotemporal costmap inference for mpc via deep inverse reinforcement learning,” IEEE Robotics and Automation Letters, 2022.
  20. J. Garcıa and F. Fernández, “A comprehensive survey on safe reinforcement learning,” Journal of Machine Learning Research, vol. 16, no. 1, pp. 1437–1480, 2015.
  21. M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu, “Safe reinforcement learning via shielding,” in Conference on Artificial Intelligence.   AAAI, 2018.
  22. D. Isele, A. Nakhaei, and K. Fujimura, “Safe reinforcement learning on autonomous vehicles,” in International Conference on Intelligent Robots and Systems.   IEEE/RSJ, 2018.
  23. Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th international conference on software engineering.   ACM, 2018, pp. 303–314.
  24. C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2722–2730.
  25. M. Bojarski, P. Yeres, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, and U. Muller, “Explaining how a deep neural network trained with end-to-end learning steers a car,” arXiv preprint arXiv:1704.07911, 2017.
  26. C. Choi, A. Patil, and S. Malla, “Drogon: A causal reasoning framework for future trajectory forecast,” in Proceedings of the Conference on Robot Learning 2020.   IEEE, 2020.
  27. A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  28. A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2255–2264.
  29. J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2015-August, pp. 1094–1099, 2015.
  30. P. Polack, F. Altché, B. d’Andréa Novel, and A. de La Fortelle, “The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?” in 2017 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2017, pp. 812–818.
  31. H. Febbo and D. Isele, “Accurate trajectory following for automated vehicles in dynamic environments,” in 2020 American Control Conference (ACC), 2020.
  32. T. Tashiro, “Vehicle steering control with mpc for target trajectory tracking of autonomous reverse parking,” in 2013 IEEE International Conference on Control Applications.   IEEE, 2013.
  33. R. Attia, R. Orjuela, and M. Basset, “Combined longitudinal and lateral control for automated vehicle guidance,” Vehicle System Dynamics, vol. 52, no. 2, pp. 261–279, 2014.
  34. J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in 2015 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2015, pp. 1094–1099.
  35. A. Carvalho, Y. Gao, A. Gray, H. E. Tseng, and F. Borrelli, “Predictive control of an autonomous ground vehicle using an iterative linearization approach,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).   IEEE, 2013, pp. 2335–2340.
  36. Y. Gao, T. Lin, F. Borrelli, E. Tseng, and D. Hrovat, “Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads,” in Dynamic systems and control conference, vol. 44175, 2010, pp. 265–272.
  37. C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, “Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization,” ACM Transactions on Mathematical Software (TOMS), vol. 23, no. 4, pp. 550–560, 1997.
  38. A. Kandel, S. Park, H. E. Perez, G. Kim, Y. Choi, H. J. Ahn, W. T. Joe, and S. J. Moura, “Distributionally robust surrogate optimal control for large-scale dynamical systems,” in 2020 American Control Conference (ACC).   IEEE, 2020, pp. 2225–2231.
  39. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” arXiv preprint arXiv:1711.03938, 2017.
  40. M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E, vol. 62, no. 2, p. 1805, 2000.
  41. A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model mobil for car-following models,” Transportation Research Record, vol. 1999, no. 1, pp. 86–94, 2007.
  42. J. Wei, J. M. Dolan, and B. Litkouhi, “Autonomous vehicle social behavior for highway entrance ramp management,” in Intelligent Vehicles Symposium (IV), 2013 IEEE.   IEEE, 2013, pp. 201–207.
  43. M. Sundermeyer, R. Schlüter, and H. Ney, “Lstm neural networks for language modeling,” in Thirteenth annual conference of the international speech communication association, 2012.
  44. J. Alman and V. V. Williams, “A refined laser method and faster matrix multiplication,” in Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA).   SIAM, 2021, pp. 522–539.
  45. S. Valluri and V. Kapila, “Stability analysis for linear/nonlinear model predictive control of constrained processes,” in Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No. 98CH36207), vol. 3.   IEEE, 1998, pp. 1679–1683.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Sangjae Bae (31 papers)
  2. David Isele (38 papers)
  3. Alireza Nakhaei (13 papers)
  4. Peng Xu (357 papers)
  5. Alexandre Miranda Anon (4 papers)
  6. Chiho Choi (24 papers)
  7. Kikuo Fujimura (22 papers)
  8. Scott Moura (28 papers)
Citations (19)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now