Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction. To this end, recent works explore two-stage prediction frameworks where coarse trajectories are first proposed, and then used to select critical context information for trajectory refinement. However, they either incur a large amount of computation or bring limited improvement, if not both. In this paper, we introduce a novel scenario-adaptive refinement strategy, named SmartRefine, to refine prediction with minimal additional computation. Specifically, SmartRefine can comprehensively adapt refinement configurations based on each scenario's properties, and smartly chooses the number of refinement iterations by introducing a quality score to measure the prediction quality and remaining refinement potential of each scenario. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. Experiments on Argoverse (1 & 2) show that our method consistently improves the prediction accuracy of multiple state-of-the-art prediction models. Specifically, by adding SmartRefine to QCNet, we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission. Comprehensive studies are also conducted to ablate design choices and explore the mechanism behind multi-iteration refinement. Codes are available at https://github.com/opendilab/SmartRefine/

翻译：预测周围智能体未来运动对于自动驾驶汽车在动态人机混合环境中安全运行至关重要。上下文信息（如道路地图和周围智能体状态）为运动行为预测提供了关键的几何与语义信息。为此，近期研究探索了两阶段预测框架：首先提出粗糙轨迹，然后利用其筛选关键上下文信息进行轨迹细化。然而，这类方法要么计算量巨大，要么改进效果有限，甚至两者兼有。本文提出一种新颖的场景自适应细化策略——SmartRefine，以最小额外计算代价实现预测细化。具体而言，SmartRefine能根据每个场景的特性全面自适应调整细化配置，并通过引入质量分数衡量各场景的预测质量与剩余细化潜力，智能选择细化迭代次数。SmartRefine被设计为通用且灵活的方法，可无缝集成至大多数最先进的运动预测模型中。在Argoverse（1和2）上的实验表明，该方法能持续提升多个先进预测模型的预测精度。特别地，通过将SmartRefine集成至QCNet，我们在提交时超越了Argoverse 2排行榜（单智能体赛道）上所有未集成模型融合方法的工作。此外，我们还进行了全面研究以消融设计选择并探索多迭代细化机制的开源代码见https://github.com/opendilab/SmartRefine/。