Evaluating Performance of Extended Kalman Filter Based Adaptive Cruise Control Using PID Controller

摘要

Adaptive cruise control (ACC), a common feature in an autonomous vehicle, is intended to automatically adjust the vehicle speed and maintain a safe distance from its preceding vehicle to avoid a collision. The main challenge is to filter the sensor data accurately, and the control system can make a decision quickly. This paper proposed a control method for ACC using the Extended Kalman filter (EKF) and a Proportional Integral Derivative (PID) controller, which can estimate the acceleration or braking of the preceding vehicle by adjusting the speed of the following vehicle. The proposed control method is assessed under various PID parameters using a Genetic Algorithm (GA) to optimize the ACC system using four loss metrics: (1) throttle loss, which accounts for fuel usage, and is proportional to the throttle setting; (2) ride quality, which is penalized by an excessive jerk (the first derivative of acceleration); (3) a distance penalty, which measures how far compared to the safe distance setpoint; and (4) a speed penalty, which measures how close the ACC gets to the desired speed setpoint. The proposed control method is evaluated by the experiments done using the Mississippi State University's Autonomous Vehicle Simulator (MAVS), a high-fidelity physics-based driving simulator. The GA determines PID parameters that minimize the overall loss. With the PID controller values, the ACC can change the speed and distance of the following vehicle on the MAVS platform and provide a good ride. Both the individual and total scores show good performance for the ACC system.