Real-Time Detection and Prediction of Relative Motion of Moving Objects in Autonomous Driving

Autonomous driving vehicles must have the ability to identify and predict behaviors of surrounding moving objects (e.g., other vehicles, cyclists, and pedestrians) in real-time. This is especially true in urban environments, where interactions become more complex due to high volumes of traffic. The work in this paper harnesses the Dempster-Shafer (DS) theoretic framework's ability to capture and account for various types of evidence uncertainty to develop a robust event detection and prediction model, which is appropriately calibrated to account for the underlying uncertainty so that it may be employed to arrive at a more informed decision.