自動運転技術企業のモーショナル（Motional）は、自動運転車に人間に近い推論と意思決定を学習させることを目的とした世界初の推論特化型オープンデータセット「nuReasoning」を公開した。

Motional announced the release of nuReasoning on September 8, 2026, describing it as the world’s first reasoning-centric, long-tail scenario open dataset for autonomous vehicles, and launched a companion research challenge at the European Conference on Computer Vision in Sweden. The dataset was created in partnership with the UCLA Mobility Lab and its director, Professor Jiaqi Ma. The Motional announcement positions nuReasoning as training material for end-to-end autonomous systems, targeting the rare edge cases where perception alone is insufficient. Vision-Language-Action models combine scene recognition with decisions about how to act, and Motional states that such models need training data that conveys the logic behind a driving action rather than the correct action alone. The dataset is designed to teach models spatial relationships, driving-decision reasoning, risk anticipation, and consideration of alternative outcomes. Dataset Composition nuReasoning contains 20,000 long-tail scenarios, each a video clip of at least 20 seconds embedded with human-verified reasoning annotation. The events were mined from Motional fleet data collected in Las Vegas, Pittsburgh, Los Angeles, Boston, and Singapore, and the company reports more than 105 hours of reasoning-intensive edge cases, including unusual pedestrian activity, work zones and night road construction, animal crossings, and limited-visibility scenarios. The annotation set totals...

Motional's nuReasoning release includes 20,000 long-tail driving clips, 247,000 reasoning annotations and a new evaluation challenge.

Motional has launched nuReasoning, an innovative open dataset designed to improve the decision-making abilities of autonomous vehicles by enhancing their human-like reasoning skills.