A car following model with an attention-based cognitive framework: theory, application, and statistical analysis

Abstract

Traffic simulators are essential for testing autonomous driving algorithms, and they require driver models that accurately emulate human behavior to reflect real traffic conditions. This thesis focuses on developing human driver models to be used in these simulators. The limitations of fixed driver models, which do not adapt to new information, are addressed by introducing an attention-based learning mechanism inspired by human memory. This mechanism is integrated into a multi-type car-following model developed in this study. Unlike existing models, this approach allows the ego driver’s decisions to be influenced by both the vehicle in front and the driver behind them. The predictive capabilities of the proposed model is demonstrated using human driving data and a comprehensive statistical analysis of the model parameter distributions is provided. This analysis shows how the model captures general behavioral tendencies across different data sets, enhancing the understanding of interactions between human drivers and providing more realistic simulations for testing purposes. Finally, a step by step guide for using the model in the development of high-fidelity traffic models is presented.