Development of a Connected and Automated Vehicle Longitudinal Control Model

It is envisioned that, in the future, most vehicles on our roadway will be controlled autonomously and will be connected via vehicle to everything (V2X) wireless communication networks. Developing a connected and automated vehicle (CAV) longitudinal controller, which will consider safety, comfort and operational efficiency simultaneously, is a challenge. A CAV longitudinal controller is a complex system where a vehicle senses immediate upstream vehicles using its sensors and receives information about its surroundings via wireless connectivity, and move forward accordingly. In this study, we develop an information-aware driver model (IADM) that utilizes information regarding an immediate upstream vehicle of a subject CAV through CAV sensors and V2X connectivity while considering passenger comfort and operational efficiency along with maintaining safety gap for longitudinal vehicle motion of the autonomous vehicle. Unlike existing driver models for longitudinal control, the IADM intelligently fuses data received from in vehicle sensors, and immediate upstream vehicles of the subject CAV through wireless connectivity, and IADM parameters do not need to be calibrated for different traffic states, such as congested and non congested traffic conditions. It only requires defining the subject CAVs maximum acceleration and deceleration limit, and computation time that is needed to update the subject CAVs trajectory from its previous state. Our analyses suggest that the IADM (i) is able to maintain safety using a newly defined safe gap function depending on the speed and reaction time of a CAV; (ii) shows local stability and string stability and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on the passenger preferences.