Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users. To effectively defend against adversaries, it is required to not only test autonomous cars for finding driving errors, but to improve the robustness of the cars to these errors. To this end, in this paper, we propose a two-step methodology for autonomous cars that consists of (i) finding failure states in autonomous cars by training the adversarial driving agent, and (ii) improving the robustness of autonomous cars by retraining them with effective adversarial inputs. Our methodology supports testing ACs in a multi-agent environment, where we train and compare adversarial car policy on two custom reward functions to test the driving control decision of autonomous cars. We run experiments in a vision-based high fidelity urban driving simulated environment. Our results show that adversarial testing can be used for finding erroneous autonomous driving behavior, followed by adversarial training for improving the robustness of deep reinforcement learning based autonomous driving policies. We demonstrate that the autonomous cars retrained using the effective adversarial inputs noticeably increase the performance of their driving policies in terms of reduced collision and offroad steering errors.
翻译:众所周知,自治汽车很容易受到会损害汽车安全并对其他道路使用者构成危险的对抗性攻击。为了有效保护对手,不仅需要测试自主汽车以发现驾驶错误,而且需要提高汽车的稳健性以适应这些错误。为此,我们在本文件中提出了自治汽车的两步方法,其中包括:(一) 通过培训对抗性驾驶员,在自主汽车中发现失灵状态,以及(二) 通过以有效的对抗性投入对自主汽车进行再培训,提高自主汽车的稳健性。我们的方法支持在多试管环境中测试AC。我们在此环境中,培训和比较关于两个定制汽车奖赏功能的对抗性汽车政策,以测试自主汽车的驾驶控制决定。我们在基于愿景的高度忠诚的城市驾驶模拟环境中进行了试验。我们的结果显示,对抗性试验可以用来寻找错误的自主驾驶行为,然后进行对抗性训练,以提高基于深度强化性自主驾驶政策的稳健性。我们证明,使用有效的对抗性投入对自主汽车进行再培训,明显提高了其驾驶政策在减少碰撞和越轨错误方面的影响。