FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning

FlowR2A introduces a novel approach to multimodal driving planning by integrating scoring-based and anchor-based methods through a generative model that learns reward-conditioned action distributions. Utilizing a flow-matching decoder, it leverages dense trajectory-reward pairs to enhance the correlation between actions and their outcomes across multiple dimensions, including safety and compliance. This model achieves state-of-the-art performance on NAVSIM v1 and v2 benchmarks, offering high-quality proposals and improved sampling control, which is crucial for practitioners developing robust AI-driven driving systems.