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

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.

[1]  Rizwan Ullah,et al.  Dynamic Stability Enhancement Using Fuzzy PID Control Technology for Power System , 2019, International Journal of Control, Automation and Systems.

[2]  Fanny Kobiela,et al.  Autonomous emergency braking , 2010 .

[3]  Hairi Zamzuri,et al.  Autonomous emergency braking system with potential field risk assessment for frontal collision mitigation , 2017, 2017 IEEE Conference on Systems, Process and Control (ICSPC).

[4]  Vicente Milanés Montero,et al.  Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  Rifky Ismail,et al.  Development of Low-Cost Autonomous Emergency Braking System (AEBS) for an Electric Car , 2018, 2018 5th International Conference on Electric Vehicular Technology (ICEVT).

[6]  Guofa Li,et al.  Effectiveness of Flashing Brake and Hazard Systems in Avoiding Rear-End Crashes , 2014 .

[7]  Alfian Djafar,et al.  Design car braking system using Mamdani Fuzzy Logic Control , 2017, 2017 4th International Conference on Electric Vehicular Technology (ICEVT).

[8]  Pongsathorn Raksincharoensak,et al.  Shared Control in Risk Predictive Braking Maneuver for Preventing Collisions With Pedestrians , 2016, IEEE Transactions on Intelligent Vehicles.

[9]  Jian Lu,et al.  Safety Differences between Novice and Experienced Drivers under Car-Following Situations , 2011 .

[10]  Kaiming Yang,et al.  Autonomous emergency braking based on radial basis function neural network variable structure control for collision avoidance , 2017, 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[11]  Yizhen Zhang,et al.  A new threat assessment measure for collision avoidance systems , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[12]  Pongsathorn Raksincharoensak,et al.  Motion planning via optimization of risk quantified by collision velocity accompanied with AEB activation , 2017, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES).

[13]  Chao Qu,et al.  A High Precision and Efficient Time-to-Collision Algorithm for Collision Warning Based V2X Applications , 2018, 2018 2nd International Conference on Robotics and Automation Sciences (ICRAS).

[14]  Wei Yang,et al.  Lateral distance detection model based on convolutional neural network , 2019 .

[15]  Jie Liu,et al.  A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3 , 2018, Sensors.

[16]  Mattias Bengtsson,et al.  Collision Warning with Full Auto Brake and Pedestrian Detection - a practical example of Automatic Emergency Braking , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[17]  Chang Liu,et al.  Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles , 2018 .

[18]  Jessica B. Cicchino,et al.  Real-world effects of rear automatic braking and other backing assistance systems. , 2019, Journal of safety research.

[19]  Bo Cheng,et al.  Drivers’ Braking Behaviors in Different Motion Patterns of Vehicle-Bicycle Conflicts , 2019 .

[20]  Bo Tang,et al.  Pedestrian protection using the integration of V2V and the Pedestrian Automatic Emergency Braking System , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[21]  Guofa Li,et al.  Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts. , 2017, Accident; analysis and prevention.

[22]  Stratis Kanarachos,et al.  Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study , 2019, IEEE Transactions on Intelligent Transportation Systems.

[23]  Qiang Yi,et al.  Certainty and Critical Speed for Decision Making in Tests of Pedestrian Automatic Emergency Braking Systems , 2016, IEEE Transactions on Intelligent Transportation Systems.

[24]  Yi Yang,et al.  Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision , 2018, IEEE Sensors Journal.

[25]  Bo Cheng,et al.  Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations , 2019, Mechanical Systems and Signal Processing.

[26]  Jessica B. Cicchino,et al.  Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates. , 2017, Accident; analysis and prevention.

[27]  Toshihiro Hiraoka,et al.  Collision Risk Evaluation Index Based on Deceleration for Collision Avoidance (Second Report) , 2009 .

[28]  M. A. Abu,et al.  Automated car braking system: Using neural network system via Labview environment , 2012, 2012 IEEE Conference on Open Systems.