# CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.14903 · HTML: https://mlsanity.com/paper/2507.14903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- 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, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://mlsanity.com/paper/1910.04586/) (2019)
- [Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2011.04752/) (2020)
- [Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook](https://mlsanity.com/paper/2107.01110/) (2021)
- [A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles](https://mlsanity.com/paper/2003.04371/) (2020)
- [Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2306.15968/) (2023)
- [Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning](https://mlsanity.com/paper/2207.00448/) (2022)
- [Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning](https://mlsanity.com/paper/1710.06117/) (2017)
- [SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning](https://mlsanity.com/paper/2408.07644/) (2024)
- [High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation](https://mlsanity.com/paper/2503.15014/) (2025)
- [A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints](https://mlsanity.com/paper/2505.02395/) (2025)
- [A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance](https://mlsanity.com/paper/2411.08999/) (2024)

## Abstract (arXiv)

Autonomous driving demands reliable and efficient solutions to closely related problems such as decision-making and motion planning. In this work, decision-making refers specifically to highway lane selection, while motion planning involves generating control commands (such as speed and steering) to reach the chosen lane. In the context of Connected Autonomous Vehicles (CAVs), achieving both flexible and safe lane selection alongside precise trajectory execution remains a significant challenge. This paper proposes a framework called Cohesive Decision-Guided Motion Planning (CDGMP), which tightly integrates decision-making and motion planning using a Mixture of Experts (MoE) inspired architecture combined with multi-policy reinforcement learning. By coordinating multiple specialized sub-networks through a gating mechanism, the method decomposes the complex driving task into modular components. Each sub-network focuses on a specific aspect of driving, improving efficiency by activating only the most relevant modules during inference. This design also enhances safety through modular specialization. CDGMP improves the adaptability and robustness of CAVs across diverse traffic scenarios, offering a scalable solution to real-world autonomy challenges. The architectural principles behind CDGMP, especially the use of MoE, also provide a strong foundation for other high-dimensional decision and control tasks. Simulation results (available at https://youtu.be/_-4OXNHV0UY) demonstrate reliable performance in both lane selection and motion planning.