GoIRL: マルチモーダル軌道予測のためのグラフ指向逆強化学習
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
車線グラフ特徴をグリッド空間に集約し、最大エントロピー逆強化学習で報酬分布を推定して複数の実行可能な計画を生成する軌道予測手法を提案。大規模ベンチマークで最先端性能と優れた汎化性能を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Muleilan Pei, Shaoshuai Shi, Lu Zhang, Peiliang Li, Shaojie Shen
分類: cs.CV, cs.RO
原文アブストラクト
Trajectory prediction for surrounding agents is a challenging task in autonomous driving due to its inherent uncertainty and underlying multimodality. Unlike prevailing data-driven methods that primarily rely on supervised learning, in this paper, we introduce a novel Graph-oriented Inverse Reinforcement Learning (GoIRL) framework, which is an IRL-based predictor equipped with vectorized context representations. We develop a feature adaptor to effectively aggregate lane-graph features into grid space, enabling seamless integration with the maximum entropy IRL paradigm to infer the reward distribution and obtain the policy that can be sampled to induce multiple plausible plans. Furthermore, conditioned on the sampled plans, we implement a hierarchical parameterized trajectory generator with a refinement module to enhance prediction accuracy and a probability fusion strategy to boost prediction confidence. Extensive experimental results showcase our approach not only achieves state-of-the-art performance on the large-scale Argoverse & nuScenes motion forecasting benchmarks but also exhibits superior generalization abilities compared to existing supervised models.
