Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance to motion planning, our proposed architecture encodes the probabilistic occupancy map as a proxy for obtaining pre-trained state representations. By leveraging a map-aware graph formulation of the environment, our agent-centric encoder generalizes to arbitrary road networks and traffic situations. We show that our approach significantly improves the downstream performance of a reinforcement learning agent operating in urban traffic environments.
@article{arxiv.2303.04218,
title = {Deep Occupancy-Predictive Representations for Autonomous Driving},
author = {Eivind Meyer and Lars Frederik Peiss and Matthias Althoff},
journal= {arXiv preprint arXiv:2303.04218},
year = {2023}
}