# Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles

arXiv: https://arxiv.org/abs/1806.01368 · HTML: https://mlsanity.com/paper/1806.01368/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [ARC: Adversarially Robust Control Policies for Autonomous Vehicles](https://mlsanity.com/paper/2107.04487/) (2021)
- [Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking](https://mlsanity.com/paper/2109.06523/) (2021)
- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward](https://mlsanity.com/paper/2112.06185/) (2021)
- [Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation](https://mlsanity.com/paper/2203.05774/) (2022)
- [Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents](https://mlsanity.com/paper/2309.14615/) (2023)

## Abstract (arXiv)

With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an optimal adversarial agent, trained to drive the system into unsafe states. We describe the architecture and flow of this framework as a benchmarking solution, and demonstrate its efficacy via a practical case study of comparing the reliability of two collision avoidance mechanisms in response to intentional collision attempts.