# Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2003.08034 · HTML: https://mlsanity.com/paper/2003.08034/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning](https://mlsanity.com/paper/2104.08876/) (2021)
- [Robust AI Driving Strategy for Autonomous Vehicles](https://mlsanity.com/paper/2207.07829/) (2022)
- [Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation](https://mlsanity.com/paper/2310.00029/) (2023)

## Abstract (arXiv)

Deep reinforcement learning methods have been widely used in recent years for autonomous vehicle's decision-making. A key issue is that deep neural networks can be fragile to adversarial attacks or other unseen inputs. In this paper, we address the latter issue: we focus on generating socially acceptable perturbations (SAP), so that the autonomous vehicle (AV agent), instead of the challenging vehicle (attacker), is primarily responsible for the crash. In our process, one attacker is added to the environment and trained by deep reinforcement learning to generate the desired perturbation. The reward is designed so that the attacker aims to fail the AV agent in a socially acceptable way. After training the attacker, the agent policy is evaluated in both the original naturalistic environment and the environment with one attacker. The results show that the agent policy which is safe in the naturalistic environment has many crashes in the perturbed environment.