Deep Occupancy-Predictive Representations for Autonomous Driving
This work addresses the problem of feature engineering for autonomous vehicles, offering a domain-specific solution that is incremental by building on existing learning-based methods.
The paper tackles the challenge of manually specifying features for autonomous driving by learning task-relevant features through an architecture that encodes probabilistic occupancy maps as pre-trained state representations, resulting in significant performance improvements for a reinforcement learning agent in urban traffic environments.
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.