# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 1 Introduction  
Urban public transportation is the main body of urban passenger transportation in China. In recent years, the number of passenger cars in China has grown steadily [1]. Due to the heavily loaded body of the passenger car, its braking system mostly adopts pneumatic braking, which has led to a long pressure response time. If the braking timing is missed, the braking effect of the passenger car will be weakened, which will eventually lead to traffic accidents. According to statistics, about 70% of China's traffic accidents are caused by braking failure, insufficient braking force, and late braking. Insufficient braking is one of the main causes of collision accidents [2]. In recent years, automatic emergency braking (AEB) has become one of the key technologies of automobile active safety. The vehicle equipped with AEB can accurately grasp the braking time, actively brake the vehicle under dangerous conditions, and effectively avoid collision accidents [3]. [PERSON] et al. [4] analyzed the key parameters of the AEB system and proposed an optimized AEB system, which can reduce the severity of collision accidents and unavailable accidents. Accurately judging the driving scene is the premise for the operation of the AEB system. At present, radar and camera are usually used to perceive the driving scene. [PERSON] et al. [5] developed an alternative circular target for AEB system testing. When the 77 GHz millimeter wave radar system detects the target, its RCS level is the same as the actual cycle. Based on the data of 30 traffic accidents, [PERSON] et al. [6] used a simulation to prove that with the increase in the radar angle in the AEB system, the crash avoidance rate of the vehicle is increased. [PERSON] et al. [7] described the evaluation method of ADAS system sensors and tested and evaluated the performance of millimeter wave radar, LiDAR, and image sensor on site. [PERSON] et al. [8]proposed a robust design method for AEB system considering sensor measurement error. Numerical examples showed that the method can provide a reference for the design of optimal AEB decision rules. The design and application of an excellent AEB control algorithm can greatly improve the performance of the AEB system, thereby improving driving safety. [PERSON]. [9] designed a Petri net model and controller for vehicles and pedestrians in specific areas, which can avoid collisions between vehicles and pedestrians in the Petri net model. [PERSON] et al. [10] proposed a model based on BP neural network and hidden Markov model to identify the intentions of drivers in front of them, and further proposed an effective AEB model. [PERSON] et al. [11] proposed a multi-sensor fusion AEB control algorithm and verified the robustness and effectiveness of the AEB system through simulation. [PERSON] et al. [12] designed a multi-objective optimization AEB control strategy. The simulation results showed that the multi-objective optimization AEB control method had better performance than the single AEB algorithm in collision avoidance, driving comfort, and fuel economy. [PERSON] et al. [13] formulated the anti-collision of vehicles and pedestrians as a partially observable Markov decision process (POMDP), and compared the anti-collision performance of POMDP and AEB systems under various combinations through experiments. Note that many researchers verified the performance of the AEB control algorithm through simulation, as mentioned earlier [4; 10; 12; 13]. In addition, [PERSON] et al. [14] also evaluated the performance of the proposed adaptive AEB control strategy through simulation to prevent collisions with vehicles following behind. Due to the insufficient accuracy of the simulation, some researchers tested the existing AEB products or verified the developed AEB control algorithms in real vehicles [5; 7; 9]. [PERSON] et al. [15] used a real vehicle to test the pedestrian target selection algorithm on the test track. The results showed that the proposed AEB algorithm can avoid or mitigate accidents. [PERSON] et al.