Calibration of the intelligent driver model (IDM) with adaptive parameters for mixed autonomy traffic using experimental trajectory data

Autonomous vehicles (AVs) are expected and demonstrated to increase local traffic throughput and improve traffic stability. However, their car-following behaviour is not fully understood due to variations in their often black-box controllers. In this study, we calibrate the Intelligent Driver Model (IDM), as a widely used car-following model, for mixed autonomy traffic using real-world experimental trajectory data. We introduce a new variant of IDM, called adaptive IDM, by enabling real-time changes of its parameters based on prevailing traffic condition. We also include the standard deviation of velocity in the calibration objective function to capture the stop-and-go traffic behaviour. While the adaptive IDM parameters improve the AVs simulated driving behaviour, the inclusion of the standard deviation of velocity within the objective function enables reproducing the traffic oscillations observed in the experimental data. The results show that the proposed adaptive IDM and the calibration method successfully reproduce traffic patterns in mixed autonomy traffic.