No abstract

In this paper, we conduct a preliminary comparative study of classification of longitudinal driving behavior using Signal Temporal Logic (STL) formulas. The goal of the classification problem is to distinguish between different driving styles or vehicles. The results can be used to design and test autonomous vehicle policies. We work on a reallife dataset, the Highway Drone Dataset (HighD). To solve this problem, our first approach starts with a formula template and reduces the classification problem to a Mixed-Integer Linear Program (MILP). Solving MILPs becomes computationally challenging with increasing number of variables and constraints. We propose two improvements to split the classification problem into smaller ones. We prove that these simpler problems are related to the original classification problem in a way that their feasibility imply that of the original. Finally, we compare our MILP formulation with an existing STL-based classification tool, LoTuS, in terms of accuracy and execution time.