Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning

This article considers the problem of high-level decision process for autonomous vehicles on highways. The goal is to select a predictive reference trajectory among a set of candidate ones, issued from a trajectory generator. This selection aims at optimizing multi-criteria functions, such as safety, legal rules, preferences and comfort of passengers, or energy consumption. This work introduces a new framework for Multi-Criteria Decision Making (MCDM). The proposed approach adopts fuzzy logic theory to deal with heterogeneous criteria and arbitrary functions. Moreover, the consideration of uncertain vehicle's sensors data is done using the Dempster-Shafer Theory with fuzzy sets in order to provide a risk assessment. Simulation results using datasets collected under the NGSIM program are presented on car following cases, and extended to lane changing situations.