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Open AccessArticle

Research on Longitudinal Active Collision Avoidance of Autonomous Emergency Braking Pedestrian System (AEB-P)

by 1,*, 2,*, 1 and 1
1
State Key laboratory of Mechanical Transmission, College of Automotive Engineering, Chongqing University, Chongqing 400044, China
2
School of Information, Zhejiang University of Finance Economics, Hangzhou 310018, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(21), 4671; https://doi.org/10.3390/s19214671
Received: 9 September 2019 / Revised: 16 October 2019 / Accepted: 21 October 2019 / Published: 28 October 2019
(This article belongs to the Special Issue Sensors and Sensor's Fusion in Autonomous Vehicles)
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. View Full-Text
Keywords: AEB-P system; warning model; upper fuzzy neural network controller; lower PID controller; CarSim and Simulink co-simulation AEB-P system; warning model; upper fuzzy neural network controller; lower PID controller; CarSim and Simulink co-simulation
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Yang, W.; Zhang, X.; Lei, Q.; Cheng, X. Research on Longitudinal Active Collision Avoidance of Autonomous Emergency Braking Pedestrian System (AEB-P). Sensors 2019, 19, 4671.

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