用图神经网络与逆强化学习预测多模式行车轨迹，提升自动驾驶安全性。
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
- 基于图结构的逆强化学习框架，融合车道图特征生成轨迹策略。
- 在Argoverse和nuScenes上达到领先性能，多模态预测准确率更高。
- 适合自动驾驶场景中复杂交互行为建模，尤其关注泛化能力。
自动驾驶中周围车辆的轨迹预测因固有的不确定性与多模态特性而极具挑战。不同于主流依赖监督学习的数据驱动方法，本文提出一种基于图结构的逆强化学习（GoIRL）框架，该框架采用向量化的上下文表示，通过特征适配器将车道图特征有效聚合至网格空间，实现与最大熵逆强化学习范式的无缝集成，从而推断奖励分布并获取可采样的策略，生成多个合理轨迹计划。进一步地，基于采样得到的计划，构建分层参数化轨迹生成器，并引入精修模块提升预测精度，以及概率融合策略增强预测置信度。大量实验表明，该方法在大规模Argoverse与nuScenes运动预测基准上均达到当前最优表现，且相比现有监督模型展现出更强的泛化能力。
原文摘要 · Abstract (English)
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.
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