A Physics-Based Longitudinal Driver Model for Automated Vehicles

Abstract

In this study, we develop a physics-based autonomous vehicle longitudinal driver model (PAVL-DM) to move an automated vehicle (AV) forward with a minimum following gap while considering safety and passenger comfort. Unlike existing driver models for longitudinal control, PAVL-DM parameters do not need to be calibrated for different traffic states, such as congested and non-congested traffic conditions. First, we present the concept, theoretical considerations and mathematical formulations of the PAVL-DM longitudinal control model for AVs. Second, we theoretically analyze the PAVL-DM model equilibria and robustness for safety assurance and string stability. Third, we present numerical analysis related to riding comfort and a dynamic minimum following gap function for each automated vehicle in a platoon while considering the safety for the automated platoon. Our analyses suggest that the PAVL-DM (i) is able to maintain safety between a subject AV and an immediate front vehicle using a newly defined safe gap function depending on the speed and reaction time of an AV; (ii) shows local stability and string stability; and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on passengers’ corresponding preferences on driving pattern. In addition, we conducted a case study using a real-world dataset, which proves that an AV with the PAVL-DM model maintains a minimum following gap between a subject AV and an immediate front vehicle without compromising safety and passenger comfort.

Keywords

• Car-following model
• connected and automated vehicles
• cooperative adaptive cruise control
• vehicle-to-everything communication
• longitudinal control model