EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving

arXiv:2310.17540v2 Announce Type: replace-cross
Abstract: Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to handle the intricate dynamics inherent to autonomous vehicles and the interaction relationships among agents in the scene. As a result, these models have a lower model capacity, which then leads to higher prediction errors and lower training efficiency. In our research, we employ EqMotion, …

abstract agent agents arxiv autonomous autonomous driving autonomous vehicles cs.lg cs.ro driving dynamics forecasting interactions preservation relationships type understanding vehicles