Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving

arXiv:2403.07232v1 Announce Type: cross
Abstract: Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining. We consider recent trajectory prediction approaches which leverage learned anchor embeddings to predict multiple trajectories, finding that these anchor embeddings …

abstract arxiv autonomous autonomous driving behavior control cs.lg cs.ro driving forecasting however loop multimodal planning prediction progress show tractable training trajectory type urban