CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
On the map: End-to-end autonomous driving → Reinforcement learning for driving
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.
Key facts. No citing papers in the map yet (data through 7 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.
Builds on
12 of 31 references.
Closest ancestors · 2
Same direction, newest first
- 2023Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning28 Jun
- 2022Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning1 Jul
Other references · 10
- 2017Attention Is All You Needcited by 59,932
- 2019Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehiclescited by 51
- 2020Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learningcited by 17
- 2021Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlookcited by 14
- 2020A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehiclescited by 12
- 2017Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learningcited by 4
- 2025High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulationcited by 3
- 2024SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planningcited by 2
- 2025A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraintscited by 2
- 2024A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidancecited by 1
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Close in wording, no citation either way
- 2026PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts
- 2024Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach
- 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
- 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
- 2023Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
- 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
- 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
- 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment