Interaction-Aware Long-term Driving Situation Prediction

Abstract

Automated vehicles require a comprehensive understanding of the current traffic situation and their future evolution to perform safe and comfortable actions. To enable reliable long-term predictions of traffic participants the interaction among each other cannot be neglected. This contribution tackles the problem of interaction-based trajectory prediction with limited information of the situation as delivered by most on-board perception systems of nowadays automated vehicles. A Monte Carlo simulation based approach which utilizes well-known sensor models in a probabilistic manner to model the interaction between traffic participants is presented. Besides uncertainty in maneuver decisions, the maneuver execution is modeled with a probabilistic model learned from realworld data. Furthermore, the problem of limited information is addressed by a combination of the interaction-aware model with a maneuver classification algorithm providing information on the short-term maneuver. The approach is evaluated on two meaningful simulation scenarios demonstrating the advantages of interaction-based prediction in general and the handling of limited perception capabilities in specific.