In recent years, end-to-end autonomous driving models have shown promising potential by directly generating waypoints or control signals from sensor data. In particular, low-level control signals, such as steering angle and vehicle speed, directly determine vehicle motion, making their prediction accuracy and inter...
Wei Dong, Ying-Feng Cai, Xiao-Qiang Sun et al.· Scientific Reports· 0 citations
Environment perception based on multimodal data fusion is crucial for the development of autonomous driving (AD) technology. However, the existing fusion-based detection and tracking methods often struggle to efficiently utilize the multimodal complementary information. This limitation can significantly impact the safe...
Cheng Zhang, Hai Wang, Long Chen et al.· IEEE transactions on intelli...· 0 citations
In this paper, a deep operator network (DeepONet)-based feedforward–feedback control framework for autonomous vehicles’ path tracking controller is introduced. In the proposed framework, DeepONet is utilized for data-driven vehicle dynamics modeling within the feedforward control component. To improve the accuracy of l...
Ying-Feng Cai, Chenkai Tan, Hai Wang et al.· Proceedings of the Instituti...· 0 citations
Mainstream motion predictors have achieved low average forecasting errors on large-scale benchmarks, yet rare scenarios still exhibit high uncertainty due to the coexistence of multimodal behavioral ambiguity and sparse long-tail supervision. Existing augmentation-based long-tail strategies improve robustness through l...
Ziheng Lu, Ying-Feng Cai, Hai Wang et al.· IEEE Internet of Things Jour...· 0 citations
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