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

arXiv: https://arxiv.org/abs/2404.19087 · HTML: https://mlsanity.com/paper/2404.19087/

## Key facts

- Cited by 4 papers in the map (data through 14 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.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Autonomous Braking System via Deep Reinforcement Learning](https://mlsanity.com/paper/1702.02302/) (2017)
- [Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning](https://mlsanity.com/paper/2404.14497/) (2024)
- [Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling](https://mlsanity.com/paper/2306.07525/) (2023)

## Built on by (most cited first, 4 of 4)

- [INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation](https://mlsanity.com/paper/2502.00262/) (2025)
- [Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving](https://mlsanity.com/paper/2504.18931/) (2025)
- [Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline](https://mlsanity.com/paper/2606.29014/) (2026)
- [Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning](https://mlsanity.com/paper/2607.08137/) (2026)

## 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.