Self-Evaluation of Trajectory Predictors for Autonomous Driving

Abstract

Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively researched with several competing large datasets, and established methods provide promising results. However, the incorporation of scenario understanding has been sparsely investigated. It comprises two aspects. First, by means of scenario understanding, individual assumptions of an object’s behavior can be derived to adaptively predict its future motion. Second, scenario understanding enables the detection of challenging scenarios for autonomous vehicle software to prevent safety-critical situations. Therefore, we propose a method incorporating scenario understanding into the motion prediction task to improve adaptivity and avoid prediction failures. This is realized by an a priori evaluation of the scenario based on semantic information. The evaluation adaptively selects the most accurate prediction model but also recognizes if no model is capable of accurately predicting this scenario and high prediction errors are expected. The results on the comprehensive scenario library CommonRoad reveal a decrease in the Euclidean prediction error by 81.0% and a 90.8% reduction in mispredictions of our method compared to the benchmark model.

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

“…The most advanced technological solution of an autonomous vehicle should, in addition to implementing measurement functions, detecting and tracking objects and obstacles within radar perception, include an appropriate safe control algorithm [33][34][35][36][37].…”

“…A method that incorporates scenario understanding into the motion prediction task of an autonomous vehicle to improve adaptability and avoid motion prediction errors is proposed by Karle et al in [33]. To do this, they use an a priori evaluation of the scenario based on semantic information, and the evaluation adaptively selects the most accurate prediction model, but also recognizes if no model can accurately predict this scenario.…”