Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions

Abstract

Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

Vehicle State Estimation and Prediction for Autonomous Driving in a Round Intersection

“…Regarding the future work, platooning or convoying of vehicles in the form of adaptive and cooperative adaptive cruise control [25][26][27][28][29][30][31][32][33][34] on highways is a topic of high research attention. Reference [25] has used a learning approach, while reference [26] has used an algorithm based on the Frenet frame. Reference [27] has used lookahead anticipation in highway driving.…”

Energy-Optimal Adaptive Control Based on Model Predictive Control

“…Vehicle variation, which includes changes in the vehicle mass due to changes in the weight of the passengers and changes in the rolling resistance coefficient due to changing tires; environment variation, which includes changes in gravitational acceleration, wind resistance coefficient, and air density; and modeling errors, which include the effect of lateral direction control on velocity [8], the effect of state of charge (SOC) changes on battery voltage [22], and the effect of tire pressure on grip during acceleration and braking. The velocity of the front vehicle cannot be directly modeled, and the prediction of speed must have errors.…”

“…Nevertheless, conventional ACC systems have limitations in terms of energy efficiency, driver comfort, and tracking stability. To address these challenges, researchers have proposed various ACC extensions, including cooperative adaptive cruise control (CACC) [6][7][8], personalized adaptive cruise control (PACC) [9][10][11], and energy-optimal adaptive cruise control (EACC) [1,[12][13][14][15]. CACC enables multi-vehicle cooperation through vehicle communication systems, while PACC tailor sand simulates individual driving habits.…”

Modeling and Optimization of Connected and Automated Vehicle Platooning Cooperative Control with Measurement Errors

“…By incorporating various optimization objectives and constraints, these methods systematically enhance comfort, safety, energy efficiency, and other performance aspects [11]. Ren et al [12] proposed a CACC algorithm based on the frenet frame, which decouples the vehicle motion into one-dimensional motion in order to simplify the controller design and improve the efficiency of the solution. Tan et al [13] proposed a real-time predictive distributed CACC control framework that addresses time delays, actuator lag, and utilizes intent-sharingbased distributed computing to improve string stability under various traffic dynamics by formulating a Kalman-filter-based real-time current driving state prediction model, thus solving the problem using a sequential Kalman filter update process and implementing a real-time distributed MPC-based CACC controller with delay-compensated predicted initial conditions.…”