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cs.RO2025
SceneStreamer: Continuous Scenario Generation as Next Token Group Prediction
Zhenghao Peng, Yuxin Liu, Bolei Zhou
Realistic and interactive traffic simulation is essential for training and evaluating autonomous driving systems. However, most existing data-driven simulation methods rely on stat…
cs.RO2025
Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation
Yuxin Liu, Zhenghao Peng, Xuanhao Cui +1
Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-criti…
cs.RO2025
Data-Efficient Learning from Human Interventions for Mobile Robots
Zhenghao Peng, Zhizheng Liu, Bolei Zhou
Mobile robots are essential in applications such as autonomous delivery and hospitality services. Applying learning-based methods to address mobile robot tasks has gained popularit…