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 lateral dynamics, the physical model is integrated into the DeepONet architecture as prior knowledge. By incorporating experimentally validated physical models, the proposed approach captures the causal relationship between vehicle parameters (e.g. mass, tire-road friction) and vehicle dynamics, thereby enhancing the interpretability of the model. In the CarSim/Simulink simulation environment, the designed DeepONet-based feedforward–feedback controller is evaluated in two scenarios: double-lane change and oval track. The validation results demonstrate that the proposed approach achieves smaller lateral errors than other methods in both linear and nonlinear regions. Notably, when facing unknown roads and varying road friction conditions on the oval track, the DeepONet-based approach shows improvement in tracking accuracy compared to purely data-driven methods without physical model integration.
Yingfeng 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 loss rebalancing or unconstrained trajectory synthesis, but some pipelines decouple three dependent decisions: where to augment, which synthesized trajectories are behaviorally valid, and how augmented signals should be assimilated in multimodal training. Such decoupling can increase augmentation cost and reduce efficiency, while introducing support mismatch between generated samples and supervision assimilation. We propose ProtoAug, a training-time augmentation framework that jointly optimizes these decisions. ProtoAug first performs prototype-based uncertainty mining in a prototype-structured latent space to allocate augmentation budget to high-value low-confidence regions. It then conducts consistency-aware augmentation via trajectory-vocabulary retrieval, map and interaction feasibility filtering, and contrastive behavior scoring for scene-conditioned candidate selection. Finally, it introduces query-wise multisupervision with warm-up-controlled optimization, assigning original and augmented trajectories to matched prediction queries to improve mode coverage while preserving optimization stability. ProtoAug is model-agnostic and removes all auxiliary components at inference, thus keeping the original deployment complexity unchanged. Experiments on Argoverse 2 and Waymo with multimodal baselines show improved overall forecasting quality and stronger long-tail robustness.
Ziheng Lu, Yingfeng Cai, Hai Wang et al.· IEEE Internet of Things Jour...· 0 citations
