Forward collision warning algorithm optimization and calibration based on objective risk perception characteristics
(2.Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan, Hubei Province, China 430063)
(3.Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, Hunan Province, China 410205)
【Abstract】A comprehensive warning algorithm named the objective risk perception (ORP) algorithm based on the vehicle kinematics and risk perception characteristic was proposed to improve the adaptability of the advanced driver assistance system (ADAS) warning algorithm in complex driving environments. The analysis and derivation under typical risk conditions show that the proposed warning algorithm is a comprehensive mode of time headway (THW), time-to-collision (TTC), and safety margine (SM) based warning algorithms. A total of 4 500 km naturalistic driving experiments were carried out, and finally 409 valid near-collision events were extracted to calibrate the parameter thresholds of the proposed warning algorithm. The distribution characteristics of objective risk perception parameters at throttle release and brake application were obtained. The risk warning algorithm parameters were calibrated based on the near-collision events and their parameter characteristics extracted from the naturalistic driving data. The forward collision warning algorithm was developed in a simulated driving environment, and the verification experiments of the algorithm were carried out based on four risk scenarios. Research result shows that based on the parameter calibration of naturalistic driving data, the two-level warning parameter thresholds of the ORP warning algorithm are 1.4 and 0.8 s, respectively. Based on the comparison of driving behavior under typical risk conditions, the warning effectiveness of the ORP warning algorithm is slightly higher than that of the RP warning algorithm, and their effectiveness is significantly higher than that of the TTC warning algorithm. In terms of the mean minimum time-to-collision of all driving segments under the warning algorithm, the mean minimum TT values of the ORP, RP, and TTC warning algorithms are 2.02 s, 1.90 s, and 1.65 s, which shows that the ORP warning algorithm can adapt to the risk identification in complex risk environments. Based on a great many parameter calibration tests of subject vehicles and effect verification, the proposed warning algorithm can be used for the risk identification of ADASs.
【Keywords】 traffic information; advanced driver assistance system; objective risk perception; collision warning; naturalistic driving; time-to-collision;
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