这篇论文对比了视觉和轨迹两种方法，告诉你什么时候该用哪个，还发现组合起来效果最好。搞自动驾驶数据挖掘或场景验证的可以看看。
这篇论文提出一个多模态框架，结合视觉和轨迹表示（Exo-Trajectory和ScenarioFormer）进行自动驾驶场景检索。实验显示，轨迹表示在运动中心事件（如cut-ins、转弯、交通排队）上表现优秀，而视觉嵌入在外观信息重要时更有效。两者结合始终提升检索质量，取得最佳整体性能。研究基于大规模自动驾驶数据集，对比了视觉基线。
Multimodal Scenario Similarity Search for Autonomous Driving
Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query. Existing approaches typically rely on either visual representations or motion-based descriptions, making it difficult to understand their relative strengths and limitations for scenario retrieval. In this work, we present a multimodal framework for autonomous-driving scenario retrieval that combines visual and trajectory-based representations within a unified retrieval pipeline. We investigate two trajectory-based approaches: Exo-Trajectory, an explicit matching method based on surrounding-agent motion, and ScenarioFormer, a transformer-based representation learned from object trajectories using contrastive learning. We compare these approaches against strong vision-based baselines and analyze their behavior across a diverse set of driving scenarios. Experimental results show that trajectory representations provide strong retrieval performance for motion-centric events such as cut-ins, turning maneuvers, and traffic queueing, while visual embeddings excel when appearance cues are informative. Most importantly, combining visual and trajectory information consistently improves retrieval quality, yielding the best overall performance. These findings demonstrate that appearance and motion capture are complementary notions of scenario similarity and motivate multimodal retrieval systems for autonomous-driving data mining, dataset curation, and scenario-based validation.