iCluedo: Clue-Driven Agentic Investigation for Evidence-Grounded Traffic-Violation Reasoning

Traffic-violation analysis from dashcam video is a hypothesis-driven, sparse evidence reasoning problem. The decisive observations may be brief, temporally dispersed, and uncertain, yet a reliable judgment must establish who acted, where and when the behavior occurred, what happened, and which visual evidence supports the conclusion. We first introduce CVCV, a benchmark of 4.65K real-world dashcam videos covering 20 fine-grained behaviors across six California Vehicle Code (CVC) violation families. Each video is represented as a case file containing its violation labels, responsible road user, decisive timestamp, accident context, human-reviewed evidence, and grounding to the corresponding CVC section. We also introduce iCluedo, a clue driven agentic framework that addresses this problem through a progression from structured case memory, to competing hypotheses, to selective evidence acquisition, and finally map the violation to the law-grounded decision. iCluedo first organize perception tools output into a persistent, ego-centric spatio-temporal case memory that represents road users, traffic controls, events, and their evolving relationships. Segment-level hypothesis agent use this memory to generate competing violation hypotheses, which a violation investigation agent verifies through a selective READ–ASK–INSPECT–DECIDE loop. It retrieves relevant structured evidence, queries specialized scouts to resolve uncertainty, revisits the original video only when a decision-critical clue is missing, contradictory, or unreliable, and selects the interpretation best supported by the accumulated evidence. A visual witness provides observable facts from targeted video intervals, while explicit uncertainty determines whether additional evidence must be acquired. The resulting hypothesis is checked for consistency across the actor, location, time, behavior, and supporting evidence, and is then mapped deterministically to a traffic-law violation. iCluedo achieves 50.6% accuracy, outperforming four of five direct VLM baselines.