Human-like learning in car following: An attention-based driving strategy with memory-inspired adaptation

Traffic simulators are essential for testing autonomous driving algorithms, and they require driver models that accurately emulate human behavior to reflect real traffic conditions. Our study focuses on developing human driver models to be used in these simulators. We address the limitations of fixed driver models, which do not adapt to new information, by introducing an attention-based learning mechanism inspired by human memory. This mechanism is integrated into a multi-type car following model we developed. Unlike existing car following models, our approach allows the ego driver’s decisions to be influenced, without any bias, by both the vehicle in front and the vehicle behind them. We demonstrate the predictive capabilities of the proposed model using real traffic data and provide a comprehensive statistical analysis of the model parameter distributions. 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, we offer a step-by-step guide for implementing the model in the development of high-fidelity traffic simulators.