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.

为了确保自动驾驶环境的安全性并应对车人交互挑战，通过考虑行人响应的认知等级，提出了一种 LK-MRL 算法，它能够使自动驾驶车辆适应各种人类行为，并在城市交通模拟器中评估算法的作用，并展示其推理和高层次推理能力，以确保交通安全。