mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios

arXiv 2404.19087 ↗First posted 29 Apr 2024cs.RO cs.AI cs.LGcited by 4

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

Abstract (arXiv)

Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead, often overlooking potential risks from following vehicles. This oversight can lead to ineffective handling of high risk situations, such as high speed, closely spaced, multi vehicle scenarios where emergency braking by one vehicle might trigger a pile up collision. To overcome these limitations, this study introduces a novel deep reinforcement learning based algorithm for longitudinal control and collision avoidance. This proposed algorithm effectively considers the behavior of both leading and following vehicles. Its implementation in simulated high risk scenarios, which involve emergency braking in dense traffic where traditional systems typically fail, has demonstrated the algorithm ability to prevent potential pile up collisions, including those involving heavy duty vehicles.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 6 of its 29 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

6 of 29 references.

References · 6

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  4. 2017Autonomous Braking System via Deep Reinforcement Learningcited by 18
  5. 2024Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learningcited by 5
  6. 2023Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modelingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluationcited by 5
  2. 2025Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Drivingcited by 0
  3. 2026Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guidelinecited by 0
  4. 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
  2. 2026Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
  3. 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
  4. 2023Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
  5. 2024Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach
  6. 2024Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
  7. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  8. 2023Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach