صورة الغلاف

SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model

محفوظ في:
الحاوية / القاعدة:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 1570-1580
المؤلف الرئيسي: Tan, Shuhan
مؤلفون آخرون: Lambert, John, Jeon, Hong, Kulshrestha, Sakshum, Bai, Yijing, Luo, Jing, Anguelov, Dragomir, Tan, Mingxing, Jiang, Chiyu Max
منشور في:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
الموضوعات:
Pattern recognition
Simulation
Scene generation
Pedestrians
Computer vision
Traffic signals
Occlusion
Environment simulation
الوصول للمادة أونلاين:Citation/Abstract
الوسوم: إضافة وسم
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مستخلص:Conference Title: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference Start Date: 2025 June 10Conference End Date: 2025 June 17Conference Location: Nashville, TN, USAThe goal of traffic simulation is to augment a potentially limited amount of manually-driven miles that is available for testing and validation, with a much larger amount of simulated synthetic miles. The culmination of this vision would be a generative simulated city, where given a map of the city and an autonomous vehicle (AV) software stack, the simulator can seamlessly simulate the trip from point A to point B by populating the city around the AV and controlling all aspects of the scene, from animating the dynamic agents (e.g., vehicles, pedestrians) to controlling the traffic light states. We refer to this vision as CitySim, which requires an agglomeration of simulation technologies: scene generation to populate the initial scene, agent behavior modeling to animate the scene, occlusion reasoning, dynamic scene generation to seamlessly spawn and remove agents, and environment simulation for factors such as traffic lights. While some key technologies have been separately studied in various works, others such as dynamic scene generation and environment simulation have received less attention in the research community. We propose SceneDiffuser++, the first end-to-end generative world model trained on a single loss function capable of point A-to-B simulation on a city scale integrating all the requirements above. We demonstrate the city-scale traffic simulation capability of SceneDiffuser++ and study its superior realism under long simulation conditions. We evaluate the simulation quality on an augmented version of the Waymo Open Motion Dataset (WOMD) with larger map regions to support trip-level simulation.
DOI:10.1109/CVPR52734.2025.00154
المصدر:Science Database