Impact of Different Desired Velocity Profiles and Controller Gains on Convoy Driveability of Cooperative Adaptive Cruise Control Operated Platoons

Abstract

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.

A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

“…Many of the proposed upper-level control schemes utilize fuzzy logic, model predictive controls, and reinforcement-learning [16]- [19]. Research improving upper-level controls often seek to increase safety, traffic flow, and efficiency through cooperative traffic management [20], [21]. The proposed low-level control methods primarily determine the actuator inputs to achieve a necessary drivetrain output for maintaining speed.…”

Modified Cooperative Adaptive Cruise Control Strategy for Optimizing Cooperative Driving in Freeway Weaving Segments

“…The most commonly used CACC control strategy is the feedback control algorithm, including proportional and derivative (PD) strategy, proportional integral derivative (PID) strategy, model predictive control (MPC) controller, and the Smith predictor CACC controller (21)(22)(23)(24)(25)(26)(27). All of these feedback CACC control strategies are based on the output errors of the CACC systems.…”