Real-World Automated Vehicle Longitudinal Stability Analysis: Controller Design and Field Test

Although extensive research has been conducted on modeling the stable longitudinal controller of automated vehicles (AVs) to dampen traffic oscillations, the real-world performance of these controllers in actual vehicles remains uncertain. In the operation of real-world AVs, the delay between actual dynamics and the commands prevents the controller's command from being effectively implemented to dampen traffic oscillations. Thus, this study adapts the designed controllers within an AV test platform to compare the theoretically stable conditions with the actual oscillation dampening performance. Initially, we compute the stable conditions for both the traditional car-following controller, which assumes no delay, and the longitudinal controller that accounts for the dynamic response of the vehicle. Through empirical experiments, we demonstrate that the longitudinal controller predicts vehicle stability more accurately than conventional car-following controller, showing an improvement from an average prediction accuracy rate of 0.59 to 0.91. Also, the experiments uncover specific delays inherent in dynamics systems, with a response delay of 0.34 seconds. Our work makes two principal contributions to the field of AV control systems. First, it empirically validates that the longitudinal model, which accounts for the vehicle's dynamic responses, offers a more precise representation of vehicular behavior. Second, the relatively brief response delay identified expands the stability region, thereby enhancing vehicle control and safety. The longitudinal controller is critical for enhancing AV performance and reliability in dampening traffic oscillations.