This paper reviews integrated chassis control (ICC) techniques for automated ground vehicles. ICC systems aim to enhance vehicle dynamics, performance, comfort, handling, and stability by coordinating multiple subsystems such as brakes, steering, suspension, and electric motors. The paper analyzes the control structure of automated vehicles with ICC, focusing on the integration of longitudinal, lateral, and vertical systems. It discusses the challenges of overlapping control regions and the need for control allocation to optimize control actions among actuators. The paper provides a systematic overview of various control methods used in ICC and path tracking, including perception, decision-making, parameter estimation, reference generation, and controller hierarchy. It highlights the importance of control allocation in over-actuated systems and the role of sensors in vehicle state estimation. The paper also discusses the integration of ICC with automated driving (AD) systems, addressing challenges such as energy efficiency, redundancy, and precise motion control. It reviews previous studies on ICC, categorizing them into five types based on the integration of longitudinal, lateral, and vertical systems. The paper also discusses the use of sensors such as IMUs, cameras, LIDAR, and radar in AD and ICC, emphasizing their roles in perception, localization, and environment understanding. It highlights the importance of sensor fusion and data preprocessing in improving the accuracy and robustness of ICC systems. The paper concludes that ICC is crucial for enabling advanced driver assistance systems (ADAS) and automated driving, and that future research should focus on improving control strategies, fault tolerance, and energy efficiency in ICC systems.This paper reviews integrated chassis control (ICC) techniques for automated ground vehicles. ICC systems aim to enhance vehicle dynamics, performance, comfort, handling, and stability by coordinating multiple subsystems such as brakes, steering, suspension, and electric motors. The paper analyzes the control structure of automated vehicles with ICC, focusing on the integration of longitudinal, lateral, and vertical systems. It discusses the challenges of overlapping control regions and the need for control allocation to optimize control actions among actuators. The paper provides a systematic overview of various control methods used in ICC and path tracking, including perception, decision-making, parameter estimation, reference generation, and controller hierarchy. It highlights the importance of control allocation in over-actuated systems and the role of sensors in vehicle state estimation. The paper also discusses the integration of ICC with automated driving (AD) systems, addressing challenges such as energy efficiency, redundancy, and precise motion control. It reviews previous studies on ICC, categorizing them into five types based on the integration of longitudinal, lateral, and vertical systems. The paper also discusses the use of sensors such as IMUs, cameras, LIDAR, and radar in AD and ICC, emphasizing their roles in perception, localization, and environment understanding. It highlights the importance of sensor fusion and data preprocessing in improving the accuracy and robustness of ICC systems. The paper concludes that ICC is crucial for enabling advanced driver assistance systems (ADAS) and automated driving, and that future research should focus on improving control strategies, fault tolerance, and energy efficiency in ICC systems.