VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
This work addresses the problem of efficient and accurate joint traffic prediction for autonomous driving systems, representing an incremental improvement over existing occupancy flow methods.
The paper tackles the challenge of predicting consistent joint behaviors of multiple road agents in autonomous driving by proposing a novel occupancy flow fields predictor that combines image and vector encoders, achieving 3rd place on the Waymo Open Dataset and best performance in occluded occupancy and flow prediction.
Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors, it remains a challenge to efficiently predict consistent joint behaviors of multiple agents. Recently, the occupancy flow fields representation was proposed to represent joint future states of road agents through a combination of occupancy grid and flow, which supports efficient and consistent joint predictions. In this work, we propose a novel occupancy flow fields predictor to produce accurate occupancy and flow predictions, by combining the power of an image encoder that learns features from a rasterized traffic image and a vector encoder that captures information of continuous agent trajectories and map states. The two encoded features are fused by multiple attention modules before generating final predictions. Our simple but effective model ranks 3rd place on the Waymo Open Dataset Occupancy and Flow Prediction Challenge, and achieves the best performance in the occluded occupancy and flow prediction task.