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DiVE: DiT-based Video Generation with Enhanced Control

Autor: Jiang, Junpeng, Hong, Gangyi, Zhou, Lijun, Ma, Enhui, Hu, Hengtong, Zhou, Xia, Xiang, Jie, Liu, Fan, Yu, Kaicheng, Sun, Haiyang, Zhan, Kun, Jia, Peng, Zhang, Miao

Generating high-fidelity, temporally consistent videos in autonomous driving scenarios faces a significant challenge, e.g. problematic maneuvers in corner cases. Despite recent video generation works are proposed to tackcle the mentioned problem, i.e

Externí odkaz: http://arxiv.org/abs/2409.01595

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UA-Track: Uncertainty-Aware End-to-End 3D Multi-Object Tracking

Autor: Zhou, Lijun, Tang, Tao, Hao, Pengkun, He, Zihang, Ho, Kalok, Gu, Shuo, Hou, Wenbo, Hao, Zhihui, Sun, Haiyang, Zhan, Kun, Jia, Peng, Lang, Xianpeng, Liang, Xiaodan

3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing metho

Externí odkaz: http://arxiv.org/abs/2406.02147

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Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation

Autor: Ma, Enhui, Zhou, Lijun, Tang, Tao, Zhang, Zhan, Han, Dong, Jiang, Junpeng, Zhan, Kun, Jia, Peng, Lang, Xianpeng, Sun, Haiyang, Lin, Di, Yu, Kaicheng

Using generative models to synthesize new data has become a de-facto standard in autonomous driving to address the data scarcity issue. Though existing approaches are able to boost perception models, we discover that these approaches fail to improve

Externí odkaz: http://arxiv.org/abs/2406.01349

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