mlsanityData to 7 Oct 2026

Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration

arXiv 2506.04040 ↗First posted 4 Jun 2025cs.RO cs.LG cs.SYcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

The controller is one of the most important modules in the autonomous driving pipeline, ensuring the vehicle reaches its desired position. In this work, a reinforcement learning based lateral control approach, despite the imperfections in the vehicle models due to measurement errors and simplifications, is presented. Our approach ensures comfortable, efficient, and robust control performance considering the interface between controlling and other modules. The controller consists of the conventional Model Predictive Control (MPC)-PID part as the basis and the demonstrator, and the Deep Reinforcement Learning (DRL) part which leverages the online information from the MPC-PID part. The controller's performance is evaluated in CARLA using the ground truth of the waypoints as inputs. Experimental results demonstrate the effectiveness of the controller when vehicle information is incomplete, and the training of DRL can be stabilized with the demonstration part. These findings highlight the potential to reduce development and integration efforts for autonomous driving pipelines in the future.

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 9 of its 36 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

9 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving18 Feb

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  6. 2017Deep Q-learning from Demonstrationscited by 27
  7. 2021Reinforcement Learning of the Prediction Horizon in Model Predictive Controlcited by 9
  8. 2020Deep Reinforcement Learning using Cyclical Learning Ratescited by 6

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  1. 2023Learning the References of Online Model Predictive Control for Urban Self-Driving
  2. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  3. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  4. 2026Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning
  5. 2022Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
  6. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  7. 2023Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
  8. 2021Neural Network Based Model Predictive Control for an Autonomous Vehicle