论文标题
产生社会可接受的扰动，以有效评估自动驾驶汽车
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
论文作者
论文摘要
近年来，深厚的增强学习方法已被广泛用于自动驾驶汽车的决策。一个关键问题是，深层神经网络可能会对对抗性攻击或其他看不见的输入而脆弱。在本文中，我们解决了后一个问题：我们专注于产生社会上可接受的扰动（SAP），以便自动驾驶汽车（AV代理），而不是挑战性的车辆（攻击者），主要是造成撞车事故的原因。在我们的过程中，将一名攻击者添加到环境中，并通过深度加强学习来培训以产生所需的扰动。奖励的设计是使攻击者旨在以社会可接受的方式使AV代理失败。在训练攻击者之后，在原始的自然主义环境和一个攻击者的环境中都评估了代理政策。结果表明，在自然主义环境中安全的代理政策在扰动环境中发生了许多崩溃。
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.
