STFP: Simultaneous Traffic Scene Forecasting and Planning for Autonomous Driving

Chan Kim, Hyung-Suk Yoon, Seung-Woo Seo, Seong-Woo Kim

Abstract: Autonomous vehicles must be able to understand the surrounding traffic flows and predict the future traffic conditions for planning a safe maneuver. During prediction, the action of autonomous vehicles should be considered, as it influences the interaction between vehicles sharing the same traffic scene and thus influences the future traffic flow. From this perspective, not only should the prediction be considered for planning, but also the action of autonomous vehicles generated by planning should be considered for traffic scene prediction. Therefore, prediction and planning must work interactively at every time step, considering results of each other. In this paper, we present a novel learning-based framework that simultaneously forecasts a nearby traffic scene and plans a maneuver of autonomous vehicle at every time step. Through experiments, we demonstrated that the proposed method exhibits better planning performance than baselines in complex traffic conditions involving various surrounding vehicles.