Human-Inspired Learning for Car Following Models

In this paper, we propose a human-inspired learning mechanism in the context of car following models. We use a memory structure to gather information from other drivers and make inferences about their driving styles. Then, this information is used to determine the ideal driving strategy. Subsequently, the learning process between the current and the ideal driving strategies is modeled with the help of adaptive control techniques. Finally, we incorporate the proposed learning mechanism into a multi-type car following model that we introduce. The performance of the proposed method is investigated using the NGSIM traffic data set. Copyright (c) 2024 The Authors.