Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916950793256960 |
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| author | Yang, Ruining Zhou, Jingyuan Wang, Qiqing Liang, Jinhao Yang, Kaidi |
| author_facet | Yang, Ruining Zhou, Jingyuan Wang, Qiqing Liang, Jinhao Yang, Kaidi |
| contents | With recent advancements in Connected Autonomous Vehicles (CAVs), Green Light Optimal Speed Advisory (GLOSA) emerges as a promising eco-driving strategy to reduce the number of stops and idle time at intersections, thereby reducing energy consumption and emissions. Existing studies typically improve energy and travel efficiency for individual CAVs without considering their impacts on the entire mixed-traffic platoon, leading to inefficient traffic flow. While Reinforcement Learning (RL) has the potential to achieve platoon-level control in a mixed-traffic environment, the training of RL is still challenged by (i) car-following safety, i.e., CAVs should not collide with their immediate preceding vehicles, and (ii) red-light safety, i.e., CAVs should not run red lights. To address these challenges, this paper develops a platoon-centric, safe RL-based GLOSA system that uses a multi-agent controller to optimize CAV speed while achieving a balance between energy consumption and travel efficiency. We further incorporate Control Barrier Functions (CBFs) into the RL-based policy to provide explicit safety guarantees in terms of car-following safety and red-light safety. Our simulation results illustrate that our proposed method outperforms state-of-the-art methods in terms of driving safety and platoon energy consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning Yang, Ruining Zhou, Jingyuan Wang, Qiqing Liang, Jinhao Yang, Kaidi Systems and Control With recent advancements in Connected Autonomous Vehicles (CAVs), Green Light Optimal Speed Advisory (GLOSA) emerges as a promising eco-driving strategy to reduce the number of stops and idle time at intersections, thereby reducing energy consumption and emissions. Existing studies typically improve energy and travel efficiency for individual CAVs without considering their impacts on the entire mixed-traffic platoon, leading to inefficient traffic flow. While Reinforcement Learning (RL) has the potential to achieve platoon-level control in a mixed-traffic environment, the training of RL is still challenged by (i) car-following safety, i.e., CAVs should not collide with their immediate preceding vehicles, and (ii) red-light safety, i.e., CAVs should not run red lights. To address these challenges, this paper develops a platoon-centric, safe RL-based GLOSA system that uses a multi-agent controller to optimize CAV speed while achieving a balance between energy consumption and travel efficiency. We further incorporate Control Barrier Functions (CBFs) into the RL-based policy to provide explicit safety guarantees in terms of car-following safety and red-light safety. Our simulation results illustrate that our proposed method outperforms state-of-the-art methods in terms of driving safety and platoon energy consumption. |
| title | Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.12378 |