论文标题
通过巨大的痛苦（身体上的智能网络）朝着更安全的自我驾驶（）
Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)
论文作者
论文摘要
自动化车辆的神经网络由于数据可用性有限而遭受过度合适，可推广性差和未经训练的边缘案例的影响。研究人员合成了随机边缘案例的场景，以协助训练过程，尽管模拟引入了过度拟合潜在规则和功能的潜力。自动化最差的场景产生可以产生信息丰富的数据以改善自我驾驶。为此，我们引入了一个“身体上的智能网络”（痛），其中自动驾驶车辆在Carla模拟环境中积极相互作用。我们使用Dueling Double Deep Q网络（DDDQN）培训两个代理商，一个主角和一个对手，并具有优先的经验重播。耦合的网络交替寻求毛利赛，并避免碰撞，以使“防御性”回避算法增加了在非托管操作条件下的平均时间到失败和距离。训练有素的主角对环境不确定性更具弹性，而不太容易发生碰撞的碰撞，而不是没有对手的训练的代理人。
Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self driving. To this end, we introduce a "Physically Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact aggressively in the CARLA simulation environment. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The coupled networks alternately seek-to-collide and to avoid collisions such that the "defensive" avoidance algorithm increases the mean-time-to-failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary.
