自動運転のための深い占有予測表現
Deep Occupancy-Predictive Representations for Autonomous Driving
確率的占有マップを予測する自己教師あり表現学習により、都市交通環境で動作する強化学習エージェントの性能を向上させた研究。
著者: Eivind Meyer, Lars Frederik Peiss, Matthias Althoff
分類: cs.LG, cs.RO
原文アブストラクト
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.
