逆強化学習を用いた条件付き予測行動計画による人間らしい自動運転
Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
人間の運転データから逆強化学習で計画を評価するスコアリングを学習し、候補軌道ごとに他車の将来軌道を条件付き予測して人間らしい運転計画を生成する枠組みを提案した。
著者: Zhiyu Huang, Haochen Liu, Jingda Wu, Chen Lv
分類: cs.RO
原文アブストラクト
Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving this objective. Distinguished from existing learning-based methods that directly output decisions, this work introduces a predictive behavior planning framework that learns to predict and evaluate from human driving data. This framework consists of three components: a behavior generation module that produces a diverse set of candidate behaviors in the form of trajectory proposals, a conditional motion prediction network that predicts future trajectories of other agents based on each proposal, and a scoring module that evaluates the candidate plans using maximum entropy inverse reinforcement learning (IRL). We validate the proposed framework on a large-scale real-world urban driving dataset through comprehensive experiments. The results show that the conditional prediction model can predict distinct and reasonable future trajectories given different trajectory proposals and the IRL-based scoring module can select plans that are close to human driving. The proposed framework outperforms other baseline methods in terms of similarity to human driving trajectories. Additionally, we find that the conditional prediction model improves both prediction and planning performance compared to the non-conditional model. Lastly, we note that learning the scoring module is crucial for aligning the evaluations with human drivers.
