Deep Occupancy-Predictive Representations for Autonomous Driving

• URL: http://arxiv.org/abs/2303.04218v1
• Date: Tue, 7 Mar 2023 20:21:49 GMT
• Title: Deep Occupancy-Predictive Representations for Autonomous Driving
• Authors: Eivind Meyer, Lars Frederik Peiss, and Matthias Althoff
• Abstract summary: We show that 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.
• Score: 6.591194329459251
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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