Cognitive Level-$k$ Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving

TL;DR

This paper introduces a novel meta-reinforcement learning algorithm that models pedestrian responses using cognitive hierarchy theory, enhancing autonomous vehicle safety in complex urban interactions.

Contribution

It combines level-$k$ thinking with MAML to adapt to diverse pedestrian behaviors, improving safety and interaction handling in autonomous driving.

Findings

Demonstrates improved safety in urban traffic simulations.

Enables autonomous vehicles to anticipate pedestrian actions.

Shows effectiveness in heterogeneous pedestrian scenarios.

Abstract

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.