Multi-Uncertainty Aware Autonomous Cooperative Planning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913569649459200 |
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| author | Zhang, Shiyao Li, He Zhang, Shengyu Wang, Shuai Ng, Derrick Wing Kwan Xu, Chengzhong |
| author_facet | Zhang, Shiyao Li, He Zhang, Shengyu Wang, Shuai Ng, Derrick Wing Kwan Xu, Chengzhong |
| contents | Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00413 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Uncertainty Aware Autonomous Cooperative Planning Zhang, Shiyao Li, He Zhang, Shengyu Wang, Shuai Ng, Derrick Wing Kwan Xu, Chengzhong Robotics Systems and Control Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment. |
| title | Multi-Uncertainty Aware Autonomous Cooperative Planning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2411.00413 |