## 1 Introduction
This system comprises components such as a laser radar, road estimation module, collision prediction module, and automatic braking module [19]. [PERSON] et al. proposed an autonomous braking decision-making strategy based on Deep Reinforcement Learning (DRL), which took into account three key influencing factors including efficiency, accuracy, and passenger comfort [20]. [PERSON] et al. introduced a rear-end Real-Time Automatic Emergency Braking (RTAEB) system, which could help successfully avoid collisions and enlarge the final headway distance. The results showed that a TTC threshold of 1.5 s and a maximum deceleration threshold of 7.5 m/s2 could achieve the best collision avoidance effect [21]. [PERSON] et al. introduced an Advanced Emergency Braking System (AEBS) with sensor fusion, which could autonomously identify a probable forward collision and activate the vehicle braking system to brake the vehicle to avoid or mitigate the collision. In addition, it incorporated a nonlinear speed controller to assist the AEBS to apply the brakes during emergency situations [22]. [PERSON] et al. proposed an integrated Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) verification system to realize the rapid verification of the AEB control algorithm for a two-axle passenger car. It could verify the logic feasibility of the control algorithm through SIL testing and the implementation effectiveness of the control algorithm through HIL testing [23]. [PERSON] et al. proposed a dual-layer control strategy for collision avoidance during high-speed lane changes, which could track both the longitudinal and lateral trajectories well [24]. [PERSON] et al. designed a road-adaptive collision avoidance algorithm including road friction estimation, distance warning, and braking control and its effectiveness was verified through HIL simulation testing [25]. [PERSON] et al. utilizedincident data to identify the characteristics of dangerous traffic situations wherein vehicles turning right at intersections encountered pedestrians. This effort contributed to the pedestrian-AEB system development and the evaluation of safety systems for preventing collisions between right-turning vehicles and pedestrians at intersections [26]. Furthermore, a plethora of control strategies have been proposed by scholars worldwide for the Lane Keeping Assist System (LKAS). [PERSON] et al. conducted a study that indicated that LDW (Lane Departure Warning) and LKA were effective in reducing the overall number of target population crashes by 3.0% \(\pm\) 32% and 60% \(\pm\) 16%, respectively. LKA was found to avoid more crashes than LDW because LKA begins evasive action earlier than the driver [27]. [PERSON] et al. presented active disturbance rejection control as a solution for automated lane-keeping steering and confirmed its feasibility through experiments [28]. [PERSON] et al. developed an end-to-end lane-keeping system including lane detection, tracking, and control components [29]. Based on the Global Navigation Satellite System (GNSS), [PERSON] et al. developed LKA systems that were not affected by the surrounding environment such as weather and the lane marking [30]. [PERSON] et al. introduced a vision-based method for automatic lane-keeping control, wherein lateral offset was used for yaw rate tracking [31]. To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind, [PERSON] et al. proposed a control strategy based on a linear quadratic regulator using a path-tracking preview model [32]. [PERSON] et al. proposed a nonlinear control of steering wheel angle using the self-aligning torque for Electric Power Steering (EPS) in the lateral control system of autonomous vehicles [33]. On average, the probability of accidents occurring on curved roads is five times higher than on straight roads [34]. [PERSON] et al.