mlsanityData to 7 Oct 2026

Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed

arXiv 2412.16248 ↗First posted 20 Dec 2024cs.AI cs.ROcited by 0

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

Abstract (arXiv)

Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios.

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 2 of its 17 references that are in the map. First posted on arXiv 20 Dec 2024.

Builds on

2 of 17 references.

References · 2

  1. 2015Trust Region Policy Optimizationcited by 2,503
  2. 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36

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  1. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  2. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  3. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  4. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  5. 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
  6. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
  7. 2024Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
  8. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey