mlsanityData to 14 Oct 2026

PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning

arXiv 2604.27118 ↗First posted 29 Apr 2026cs.RO cs.AIcited by 0

On the map: End-to-end autonomous driving → Cooperative and V2X driving

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Abstract (arXiv)

We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomous vehicles (AVs). While existing lane-change approaches typically focus on single-agent systems or centralized multi-agent systems, we introduce a federated reinforcement learning-based multi-agent lane change system prioritizing lane changing based on vehicle destination urgency. PALCAS incorporates a novel priority-aware safe lane-change reward function to enable judicious lane-change decisions in both mandatory and discretionary scenarios. PALCAS leverages the parameterized deep Q-network (PDQN) algorithm to facilitate effective cooperation among agents, enabling both lateral and longitudinal motion controls of AVs. Extensive simulations conducted using the SUMO traffic simulator and Mosaic V2X communication framework demonstrate that PALCAS significantly improves traffic efficiency, driving safety, comfort, destination arrival rates, and merging success rates compared to baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 9 of its 36 references that are in the map. First posted on arXiv 29 Apr 2026.

Builds on

9 of 36 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 131
  3. 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 58
  4. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  5. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 46
  6. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  7. 2022Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controllercited by 7
  8. 2022RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Mergingcited by 2
  9. 2024Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learningcited by 1

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  2. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  3. 2024Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles
  4. 2025AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
  5. 2024Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
  6. 2024Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach
  7. 2025A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
  8. 2025Z-Merge: Multi-Agent Reinforcement Learning for On-Ramp Merging with Zone-Specific V2X Traffic Information ↗