Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
Source abstract
Abstract
Autonomous intersection management (AIM) is gaining increasing attention due to its crucial role in ensuring safety and efficiency. Various methods have been proposed in the literature to address the AIM problem, including traffic light optimization, connected vehicle optimization based on vehicle-to-anything (V2X) communication technology, and multi-agent autonomous systems. However, each of these approaches has its own limitations, such as parking delays, communication latency, or the lack of guaranteed collision avoidance. This paper presents a novel approach to AIM using adaptive control barrier functions (aCBFs). The proposed aCBF first estimates the power transmission efficiency and incorporates it into the CBF design to ensure collision-free operation. Compared to existing methods, the aCBF approach offers several advantages. Firstly, it eliminates parking delays caused by traffic light systems. Secondly, it can be deployed in intersections with limited network coverage, unlike IoT-based solutions that rely heavily on connectivity. Thirdly, it ensures guaranteed collision-free agent movement at intersections. Specifically, our method guarantees that the minimum distance between agents remains no less than the safe distance at all times, significantly enhancing safety. Furthermore, compared to the TriPField algorithm, our approach achieves a 95% success rate in collision avoidance, demonstrating reliability in autonomous intersection management. The effectiveness of the proposed aCBF-based AIM algorithm has been validated through simulations and experiments with multiple autonomous agent-like robots.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Autonomous Vehicle Technology and Safety · Vehicular Ad Hoc Networks (VANETs)
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita 5650871, Osaka, Japan
Graduate School of Information Science and Engineering, Ritsumeikan University, 2-150 Iwakura-cho, Ibaraki 5678570, Osaka, Japan
References (61)
Show source-provided references
1. A multiagent approach to autonomous intersection management10.1613/jair.2502 ↗
2. Filocamo, B., Alonso Ruiz, J., and Sotelo, M.A. (2019). Efficient management of road intersections for automated vehicles—The FRFP system applied to the various types of intersections and roundabouts. Appl. Sci., 10.10.3390/app10010316 ↗
3. Carlino, D., Boyles, S.D., and Stone, P. (2013, January 6–9). Auction-Based Autonomous Intersection Management. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.10.1109/ITSC.2013.6728285 ↗
4. Intelligent intersection management systems considering autonomous vehicles: A systematic literature review10.1109/ACCESS.2019.2927412 ↗
5. Cooperation of multiple connected vehicles at unsignalized intersections: Distributed observation, optimization, and control10.1109/TIE.2019.2960757 ↗
6. Decision-making framework for autonomous driving at road intersections: Safeguarding against collision, overly conservative behavior, and violation vehicles10.1109/TIE.2018.2840530 ↗
7. Ghazal, B., ElKhatib, K., Chahine, K., and Kherfan, M. (2016, January 21–23). Smart Traffic Light Control System. Proceedings of the Third International Conference on Electrical, Electronics, Computer Engineering and Their Applications (EECEA), Beirut, Lebanon.10.1109/EECEA.2016.7470780 ↗
8. Density-based traffic signal system
9. An intelligent automatic traffic light controller using embedded systems
10. A real-time vision system for nighttime vehicle detection and traffic surveillance10.1109/TIE.2010.2055771 ↗
11. A deep reinforcement learning network for traffic light cycle control10.1109/TVT.2018.2890726 ↗
12. Enhancements of V2X communication in support of cooperative autonomous driving10.1109/MCOM.2015.7355568 ↗
13. Connected vehicles—Advancements in vehicular technologies and informatics10.1109/TIE.2015.2483490 ↗
14. Autonomous vehicle-intersection coordination method in a connected vehicle environment10.1109/MITS.2017.2743167 ↗
15. Isele, D., Cosgun, A., and Fujimura, K. (2017). Analyzing knowledge transfer in deep Q-networks for autonomously handling multiple intersections. arXiv.
16. Deep learning vs. discrete reinforcement learning for adaptive traffic signal control
17. Disturbance propagation in vehicle strings10.1109/TAC.2004.835586 ↗
18. Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment10.1109/TII.2018.2822828 ↗
19. Bai, Y., Wang, Y., Xiong, X., Svinin, M., and Magid, E. (2022, January 8–10). Adaptive Multi-Agent Control with Dynamic Obstacle Avoidance in a Limited Region. Proceedings of the 2022 American Control Conference (ACC), Atlanta, GA, USA.10.23919/ACC53348.2022.9867297 ↗
20. Large-margin multi-modal deep learning for RGB-D object recognition10.1109/TMM.2015.2476655 ↗
21. Vision-based nighttime vehicle detection using CenSurE and SVM10.1109/TITS.2015.2413971 ↗
22. Multiple sensor fusion and classification for moving object detection and tracking
23. Lane-changes prediction based on adaptive fuzzy neural network10.1016/j.eswa.2017.09.025 ↗
24. Human-like decision making for autonomous driving: A noncooperative game theoretic approach10.1109/TITS.2020.3036984 ↗
25. Adaptive multi-agent coverage control with obstacle avoidance10.1109/LCSYS.2021.3087609 ↗
26. An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle10.1109/TIE.2021.3066943 ↗
27. Mihály, A., Farkas, Z., and Gáspár, P. (2020). Multicriteria autonomous vehicle control at non-signalized intersections. Appl. Sci., 10.10.3390/app10207161 ↗
28. Rehder, T., Muenst, W., Louis, L., and Schramm, D. (2016, January 1–4). Learning lane change intentions through lane contentedness estimation from demonstrated driving. Proceedings of theIEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.10.1109/ITSC.2016.7795661 ↗
29. Chen, J., Zhou, Y., Lv, Q., Deveerasetty, K.K., and Dike, H.U. (2018, January 11–13). A review of autonomous obstacle avoidance technology for multi-rotor UAVs. Proceedings of the IEEE International Conference on Information and Automation (ICIA), Wuyishan, China.10.1109/ICInfA.2018.8812473 ↗
30. Control barrier function based quadratic programs for safety critical systems10.1109/TAC.2016.2638961 ↗
31. Yan, H., Zhu, Q., Zhang, Y., Li, Z., and Du, X. (2022, January 4–6). An obstacle avoidance algorithm for unmanned surface vehicle based on a star and velocity-obstacle algorithms. Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.10.1109/ITOEC53115.2022.9734642 ↗
32. Peng, Y., Qu, D., Zhong, Y., Xie, S., Luo, J., and Gu, J. (October, January 30). Obstacle detection and obstacle avoidance algorithm based on 2-D RPLIDAR. Proceedings of the IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York City, NY, USA.
33. Kim, J.C., Pae, D.S., and Lim, M.T. (2018, January 17–20). Obstacle avoidance path planning algorithm based on model predictive control. Proceedings of the 18th IEEE International Conference on Control, Automation and Systems (ICCAS), Pyeong Chang, Republic of Korea.10.1007/s12555-019-9091-y ↗
34. Jia, F., Liu, X., Wu, J., Shi, Y., Xu, F., and Zhang, Z. (2018, January 20–22). Research on multi-AGV autonomous obstacle avoidance strategy based on improved A* algorithm. Proceedings of the 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Stuttgart, Germany.10.1109/M2VIP.2018.8600834 ↗
35. Song, X. (2022, January 28–30). Research and design of robot obstacle avoidance strategy based on multisensor and fuzzy control. Proceedings of the IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA), Dalian, China.10.1109/ICDSCA56264.2022.9988357 ↗
36. A bug-inspired algorithm for obstacle avoidance of a nonholonomic wheeled mobile robot with constraints
37. Sivaranjani, S., Nandesh, D.A., Gayathri, K., and Ramanathan, R. (2021, January 25–27). An investigation of bug algorithms for mobile robot navigation and obstacle avoidance in two-dimensional unknown static environments. Proceedings of the International Conference on Communication information and Computing Technology (ICCICT), Mumbai, India.10.1109/ICCICT50803.2021.9510118 ↗
38. Cichosz, C., and Gurocak, H. (2022, January 9–12). Collision avoidance in human-cobot work cell using proximity sensors and modified bug algorithm. Proceedings of the 2022 10th IEEE International Conference on Control, Mechatronics and Automation (ICCMA), Belval, Luxembourg.10.1109/ICCMA56665.2022.10011601 ↗
39. UAV path planning using artificial potential field method updated by optimal control theory10.1080/00207721.2014.929191 ↗
40. Ulrich, I., and Borenstein, J. (1998, January 20–20). VFH+: Reliable obstacle avoidance for fast mobile robots. Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium.
41. Development of an intelligent technique for traffic network incident detection10.1016/S0952-1976(00)00011-7 ↗
42. Perez-D’Arpino, C., Medina-Melendez, W., Guzman, J., Fermin, L., and Fernandez-Lopez, G. (2009, January 14–17). Fuzzy logic based speed planning for autonomous navigation under velocity field control. Proceedings of the 2009 IEEE International Conference on Mechatronics, Malaga, Spain.10.1109/ICMECH.2009.4957170 ↗
43. UAV autonomous collision avoidance approach10.1080/00051144.2017.1388646 ↗
44. Liu, C., Zhai, L., and Zhang, X. (2022, January 28–30). Research on local real-time obstacle avoidance path planning of unmanned vehicle based on improved artificial potential field method. Proceedings of the 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), Nanjing, China.10.1109/CVCI56766.2022.9964763 ↗
45. Lu, S.X., Li, E., and Guo, R. (2020, January 2–5). An obstacles avoidance algorithm based on improved artificial potential field. Proceedings of the 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China.10.1109/ICMA49215.2020.9233866 ↗
46. Taylor, A.J., and Ames, A.D. (2020, January 1–3). Adaptive safety with control barrier functions. Proceedings of the 2020 American Control Conference (ACC), Denver, CO, USA.10.23919/ACC45564.2020.9147463 ↗
47. Robust adaptive control barrier functions: An adaptive and data-driven approach to safety10.1109/LCSYS.2020.3005923 ↗
48. Isaly, A., Patil, O.S., Sanfelice, R.G., and Dixon, W.E. (2021, January 25–28). Adaptive safety with multiple barrier functions using integral concurrent learning. Proceedings of the 2021 American Control Conference (ACC), New Orleans, LA, USA.10.23919/ACC50511.2021.9483208 ↗
49. Cohen, M.H., and Belta, C. (2022). High order robust adaptive control barrier functions and exponentially stabilizing adaptive control Lyapunov functions. arXiv.10.23919/ACC53348.2022.9867633 ↗
50. Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance10.1109/TAC.2008.929402 ↗
51. Safe adaptive multi-agent coverage control10.1109/LCSYS.2023.3312248 ↗
52. Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems10.1016/j.automatica.2016.10.011 ↗
53. Robustness of control barrier functions for safety critical control
54. Lavretsky, E., and Gibson, T.E. (2011). Projection operator in adaptive systems. arXiv.
55. Adaptive fault-tolerant tracking control against actuator faults with application to flight control10.1109/TCST.2006.883191 ↗
56. Active fault-tolerant control of unmanned quadrotor helicopter using linear parameter varying technique10.1007/s10846-017-0535-4 ↗
57. Khalil, H.K. (2002). Nonlinear Systems, Prentice Hall. [3rd ed.].
58. Multirobot Control Using Time-Varying Density Functions10.1109/TRO.2015.2397771 ↗
59. (2024, July 05). Raspimouse_description. Available online: https://github.com/rt-net/raspimouse_description.git.
60. TriPField: A 3D Potential Field Model and Its Applications to Local Path Planning of Autonomous Vehicles10.1109/TITS.2022.3231259 ↗
61. Flexible active safety motion control for robotic obstacle avoidance: A CBF-guided MPC approach10.1109/LRA.2025.3534519 ↗