论文标题
认知水平 - $ k $ meta学习，用于安全和行人意识的自动驾驶
Cognitive Level-$k$ Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving
论文作者
论文摘要
现代自动驾驶汽车的潜在市场是巨大的，因为它们正在迅速发展。然而，与此同时，在街道穿越的情况下，已经记录了由自动驾驶造成的行人死亡事故。为了确保自动驾驶环境中的交通安全性并应对诸如JayWalking之类的车辆人类互动挑战，我们建议级别-K $ META强化学习（LK-MRL）算法。它考虑了行人反应的认知层次结构，并使自动驾驶车辆能够适应各种人类行为。 ％在学习最佳策略时考虑了行人的回应。作为一种自动驾驶车辆算法，LK-MRL将级别的级别$ k $组合到MAML中，以准备异构行人，并根据元强化学习和人类认知层次结构框架的结合来改善交叉安全性。我们在城市交通模拟器中的两个认知对抗层次结构方案中评估了该算法，并通过证明其猜想和高级推理的能力来说明其在确保道路安全方面的作用。
The potential market for modern self-driving cars is enormous, as they are developing remarkably rapidly. At the same time, however, accidents of pedestrian fatalities caused by autonomous driving have been recorded in the case of street crossing. To ensure traffic safety in self-driving environments and respond to vehicle-human interaction challenges such as jaywalking, we propose Level-$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.
