Modelling of uncertain reactive human driving behavior: a classification approach

摘要

This paper discusses a novel approach to model human driver behavior. A classification-based method is proposed to construct a reactive bound on possible human driving actions given the scenario description (such as the vehicle states and the behavior of surrounding vehicles). This approach captures the reactiveness and uncertainty of human drivers. Real human driving data is used as the positive training data, while dangerous actions sampled via a Hamilton Jacobi reachability computation constitute the negative training data. A classifier that separates the two groups is then trained via a customized L1. Support Vector Machine (SVM), and an analytical bound function is derived from the classifier which maps the state and surrounding vehicles' actions to the bound on possible actions of the human driver. The credibility of the proposed approach is analyzed under the random convex optimization framework. Potential applications of this work include the computation of safe sets, synthesis of safety guaranteed controllers for systems interacting with humans such as autonomous vehicles, and evaluation of such systems.