State Key laboratory of Mechanical Transmission, College of Automotive Engineering, Chongqing University, Chongqing 400044, China.
School of Information, Zhejiang University of Finance Economics, Hangzhou 310018, China.
Sensors (Basel). 2019 Oct 28;19(21):4671. doi: 10.3390/s19214671.
The AEB-P (Autonomous Emergency Braking Pedestrian) system has the functional requirements of avoiding the pedestrian collision and ensuring the pedestrian's life safety. By studying relevant theoretical systems, such as TTC (time to collision) and braking safety distance, an AEB-P warning model was established, and the traffic safety level and work area of the AEB-P warning system were defined. The upper-layer fuzzy neural network controller of the AEB-P system was designed, and the BP (backpropagation) neural network was trained by collected pedestrian longitudinal anti-collision braking operation data of experienced drivers. Also, the fuzzy neural network model was optimized by introducing the genetic algorithm. The lower-layer controller of the AEB-P system was designed based on the PID (proportional integral derivative controller) theory, which realizes the conversion of the expected speed reduction to the pressure of a vehicle braking pipeline. The relevant pedestrian test scenarios were set up based on the C-NCAP (China-new car assessment program) test standards. The CarSim and Simulink co-simulation model of the AEB-P system was established, and a multi-condition simulation analysis was performed. The results showed that the proposed control strategy was credible and reliable and could flexibly allocate early warning and braking time according to the change in actual working conditions, to reduce the occurrence of pedestrian collision accidents.
AEB-P（自动紧急制动行人）系统具有避免行人碰撞和确保行人生命安全的功能要求。通过研究相关的理论系统，如 TTC（碰撞时间）和制动安全距离，建立了 AEB-P 预警模型，并定义了 AEB-P 预警系统的交通安全水平和工作区域。设计了 AEB-P 系统的上层模糊神经网络控制器，并通过收集有经验的驾驶员的行人纵向防撞制动操作数据来训练 BP（反向传播）神经网络。此外，还通过引入遗传算法对模糊神经网络模型进行了优化。基于 PID（比例积分微分控制器）理论设计了 AEB-P 系统的下层控制器，实现了期望减速到车辆制动管路压力的转换。根据 C-NCAP（中国新车评估计划）测试标准设置了相关的行人测试场景。建立了 AEB-P 系统的 CarSim 和 Simulink 联合仿真模型，并进行了多条件仿真分析。结果表明，所提出的控制策略是可靠的，可以根据实际工作条件的变化灵活分配预警和制动时间，从而降低行人碰撞事故的发生。
