3 accepted papers
End-to-end autonomous driving has emerged as a promising paradigm. However, state-of-the-art methods rely heavily on Transformer architectures. The inherent quadratic complexity of Transformers restricts their ability to model long-range spatial and temporal dependencies, particularly on resource-co
Recent breakthroughs in large language models (LLMs) have not only advanced natural language processing but also inspired their application in domains with structurally similar problems—most notably, autonomous driving motion generation. Both domains involve autoregressive sequence modeling, token-b…
The reasonable trajectory prediction of surrounding traffic participants is crucial for autonomous driving. Especially, how to predict multiple plausible trajectories is still a challenging problem because of the multiple possibilities of the future. Proposal-based prediction methods address the mul…