## 1 Introduction
### Related Work  
Both ESA and AES can make a sudden lane change or evasive maneuver via steering actions to avoid a collision. The main difference between ESA and AES is the trigger mechanism. When the ESA [6, 7, 8, 9] detects the risk of collision and the steering torque from the driver, the system provides additional overlay torques to adjust the steering angle to track the evasive path without collision or directional instability. However, [PERSON] et al. [10] showed that 37.9% of drivers did not take any avoidance maneuvers when encountering the approaching danger. Therefore, collision avoidance cannot be guaranteed for inattentive maneuvers with late steering actions from the driver. Unlike ESA, the AES [11; 12; 13; 14; 15] can steer automatically when the detected risk of collision is over the designed threshold. Three types of planning approaches, search-based, optimization-based, and parameter-based, are commonly used for generating a desired path to avoid the collision. Search-based methods [16; 17; 18] are designed to generate an action set of multiple drivable trajectories, allowing an adjacent behavior planner to pick the most appropriate action for the global state in the scene. Optimization-based methods [19; 20; 21] plan the path from a mapping run and offline compute an optimal trajectory around the track using the trajectory optimization module to maximize the progress rate, and minimize slip angle cost and input rate. However, the first and second types of planning approaches are not easy to implement on production control units due to high computation loads. Parameter-based methods [7] including circular, sine/cosine, s-shape, polynomial, and trapezoidal-acceleration-profile (TAP) trajectories are designed by specifying the desired lateral displacement for a lane change maneuver that can also be employed for collision avoidance. Among the parameter-based methods, only TAP can consider transition time and ride comfort at the same time [22]. Two types of path tracking controllers, model-free and model-based, are commonly used for tracking an evasive path. If the dynamic model of the plant to be controlled is not needed for the controller design, it is classified as the model-free approach. The conventional model-free controller calculates the steering angle command based on the lateral displacement error and the heading error at the previewed point [23; 24; 25]. Machine learning can also be classified as the model-free approach. A neural network model can be trained to generate the steering action using the dataset of human drivers [26] or deep reinforcement learning [27]. However, the major challenge is the difficulty in proving the safety issue of these methods [28]. As for model-based approaches, the single-input single-output (SISO) model predictive control (MPC) is often used to obtain the vehicle steering angle based on a four-state bicycle model, which is used to predict the future lateral displacement of a vehicle [29; 30; 31]. Differential braking via electric hydraulic braking (EHB) can be used to generate the desired direct yaw moment (DYM) to improve path tracking performance. [PERSON] et al. [32] proposed a multi-input multi-output (MIMO) MPC controller that uses four-wheel longitudinal slip ratios and front wheel steering angle as the control input to minimize errors in heading angle, yaw rate and lateral displacement. Their simulation results showed that path error can be reduced by 50%. However, intensive computations are required for online linearization and optimization due to the complexities of the prediction model with nonlinear tire dynamics. [PERSON] et al. [33] proposed a MIMO MPC controller to determine the optimal front lateral force and the yaw moment to improve the yaw rate responsiveness for collision avoidance without decreasing the stability. [PERSON] et al. [34] proposed a MIMO MPC controller for AES using front wheel steering angle and direct yaw-moment control (DYC) to enhance the performance of path tracking.