$IA(MP)^{2}$: Framework for Online Motion Planning Using Interaction-Aware Motion Predictions in Complex Driving Situations

Motion planning is a process of constant negotiation with the rest of the traffic agents and is highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause the motion planning algorithm to adopt overly conservative or reckless behaviors that can eventually become a dangerous driving situation. This article presents a framework integrating motion planning and interaction-aware motion prediction algorithms, which interact with each other and are able to run in real-time on complex areas such as roundabouts or intersections. The proposed motion prediction strategy generates a multi-modal probabilistic estimation of the future positions and intentions of the surrounding vehicles by taking into account traffic rules, vehicle interaction, road geometry and the reference trajectory of the ego-vehicle; the resulting predictions are fed into a sampling-based maneuver and trajectory planning algorithm that identifies the possible collision points for every generated trajectory candidate and acts accordingly. This framework enables the automated driving system to have a more agile behavior than other strategies that use more simplistic motion prediction models and where the planning stage does not provide feedback. The approach has been successfully evaluated and compared with a state-of-art approach in highly-interactive scenarios generated from public datasets and real-world situations in a software-in-the-loop simulation system.