Probe-and-Release Coordination of Platoons at Highway Bottlenecks with Unknown Parameters
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911255577493504 |
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| author | Gao, Yi Xiong, Xi Johansson, Karl H. Jin, Li |
| author_facet | Gao, Yi Xiong, Xi Johansson, Karl H. Jin, Li |
| contents | This paper considers coordination of platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks in the face of three practically important factors, viz. time-varying traffic demand, random CAV platoon sizes, and capacity breakdowns. Platoon coordination is essential to smoothen the interaction between CAV platoons and non-CAV traffic. Based on a fluid queuing model, we develop a "probe-and-release" algorithm that simultaneously estimates environmental parameters and coordinates CAV platoons for traffic stabilization. We show that this algorithm ensures bounded estimation errors and bounded traffic queues. The proof builds on a Lyapunov function that jointly penalizes estimation errors and traffic queues and a drift argument for an embedded Markov process. We validate the proposed algorithm in a standard micro-simulation environment and compare against a representative deep reinforcement learning method in terms of control performance and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probe-and-Release Coordination of Platoons at Highway Bottlenecks with Unknown Parameters Gao, Yi Xiong, Xi Johansson, Karl H. Jin, Li Systems and Control This paper considers coordination of platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks in the face of three practically important factors, viz. time-varying traffic demand, random CAV platoon sizes, and capacity breakdowns. Platoon coordination is essential to smoothen the interaction between CAV platoons and non-CAV traffic. Based on a fluid queuing model, we develop a "probe-and-release" algorithm that simultaneously estimates environmental parameters and coordinates CAV platoons for traffic stabilization. We show that this algorithm ensures bounded estimation errors and bounded traffic queues. The proof builds on a Lyapunov function that jointly penalizes estimation errors and traffic queues and a drift argument for an embedded Markov process. We validate the proposed algorithm in a standard micro-simulation environment and compare against a representative deep reinforcement learning method in terms of control performance and computational efficiency. |
| title | Probe-and-Release Coordination of Platoons at Highway Bottlenecks with Unknown Parameters |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.06026 |