The potential market for modern self-driving cars is enormous, as they are developing remarkably rapidly. At the same time, however, accidents of pedestrian fatalities caused by autonomous driving have been recorded in the case of street crossing. To ensure traffic safety in self-driving environments and respond to vehicle-human interaction challenges such as jaywalking, we propose Level-$k$ Meta Reinforcement Learning (LK-MRL) algorithm. It takes into account the cognitive hierarchy of pedestrian responses and enables self-driving vehicles to adapt to various human behaviors. %which takes into account pedestrian responses while learning the optimal strategies. As a self-driving vehicle algorithm, the LK-MRL combines level-$k$ thinking into MAML to prepare for heterogeneous pedestrians and improve intersection safety based on the combination of meta-reinforcement learning and human cognitive hierarchy framework. We evaluate the algorithm in two cognitive confrontation hierarchy scenarios in an urban traffic simulator and illustrate its role in ensuring road safety by demonstrating its capability of conjectural and higher-level reasoning.

翻译：现代自动驾驶汽车发展极为迅速，其潜在市场十分广阔。然而与此同时，在行人过街场景中，已记录到因自动驾驶导致行人死亡的事故。为确保自动驾驶环境中的交通安全，并应对乱穿马路等车辆-行人交互挑战，我们提出了Level-$k$元强化学习（LK-MRL）算法。该算法考虑了行人反应的认知层级，使自动驾驶车辆能够适应多种人类行为。作为自动驾驶车辆算法，LK-MRL将Level-$k$思维融入MAML，以应对异质行人，并基于元强化学习与人类认知层级框架的结合提高交叉口安全性。我们在城市交通模拟器中设置了两种认知对抗层级场景对算法进行评估，通过展示其推测性及更高层级推理能力，阐明了该算法在保障道路安全中的作用。