Interactive scene prediction for automotive applications

TLDR

Assessing future vehicle trajectories for safety requires predicting the motion of all traffic participants, and existing approaches inadequately account for their mutual influence, reducing reliability in real traffic. This work presents a framework for motion prediction of vehicles and safety assessment of traffic scenes. The framework models traffic participants’ mutual influence via an optimization‑inspired approach, computes collision probabilities for each maneuver, and uses safety evaluation under the assumption that drivers avoid collisions to realize predictions. The framework is applicable to driver assistance and autonomous driving, and simulation and real‑world results demonstrate its functionality.

Abstract

In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality.