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DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Feb. 29, 2024, 5:46 a.m. | Shuo Sun, Zekai Gu, Tianchen Sun, Jiawei Sun, Chengran Yuan, Yuhang Han, Dongen Li, Marcelo H. Ang Jr
cs.CV updates on arXiv.org arxiv.org
Abstract: Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen …
abstract annotations arxiv autonomous autonomous driving autonomous driving systems collection cs.cv cs.ro data data collection datasets development diverse diversity driving process reliance scratch support systems traffic type validation world
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