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.
We propose a novel framework to differentiate between vehicle trajectories originating from human and non-human drivers by constructing a data-driven boundary using parametric signal temporal logic (STL). Such construction allows us to evaluate the trajectories, detect rare-events, and reduce the uncertainty of driver behaviors when it assumes the form of a disturbance in control synthesis and evaluation problems. We train a classifier that separates admissible (i.e. human) examples - which arise from real-world demonstrations - and inadmissible (i.e. non-human) examples that are generated by falsifying specifications synthesized from the same real-world driving data. Proceeding in this fashion allows for finding a reasonable boundary of human behaviors exhibited in real-world driving records. The framework is demonstrated using a case study involving a human-driven vehicle approaching a signalized intersection.
We propose a novel framework to differentiate between vehicle trajectories originating from human and non-human drivers by constructing a data-driven boundary using parametric signal temporal logic (STL). Such construction allows us to evaluate the trajectories, detect rare-events, and reduce the uncertainty of driver behaviors when it assumes the form of a disturbance in control synthesis and evaluation problems. We train a classifier that separates admissible (i.e. human) examples - which arise from real-world demonstrations - and inadmissible (i.e. non-human) examples that are generated by falsifying specifications synthesized from the same real-world driving data. Proceeding in this fashion allows for finding a reasonable boundary of human behaviors exhibited in real- world driving records. The framework is demonstrated using a case study involving a human-driven vehicle approaching a signalized intersection.
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