# Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.12378 · HTML: https://mlsanity.com/paper/2509.12378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 7 of 7)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles](https://mlsanity.com/paper/2105.11640/) (2021)
- [A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles](https://mlsanity.com/paper/1903.08712/) (2019)
- [Correct-By-Construction Design of Adaptive Cruise Control with Control Barrier Functions Under Safety and Regulatory Constraints](https://mlsanity.com/paper/2203.14110/) (2022)
- [Modified Dynamic Programming Algorithms for GLOSA Systems with Stochastic Signal Switching Times](https://mlsanity.com/paper/2211.12159/) (2022)
- [Adaptive Frequency Green Light Optimal Speed Advisory based on Hybrid Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2306.04660/) (2023)

## Abstract (arXiv)

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.