K
Keqiang Li
Publications - 85
Citations - 853
Keqiang Li is an academic researcher. The author has contributed to research in topics: Noise & Signal. The author has an hindex of 16, co-authored 85 publications receiving 853 citations.
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Patent
State estimating method for automobile start illumination type lead-acid storage battery
Zhengmin Gu,Keqiang Li,Xiaomin Lian,Liang Lu,Yugong Luo,Jianqiang Wang,Diange Yang,Xinfeng Zhang,Sifa Zheng +8 more
TL;DR: In this paper, a state estimation method for an automobile start illumination type lead-acid storage battery is proposed. But the method is not suitable for the case of a single battery.
Patent
Pre-alarm method for preventing rear-end collision by following car
Jianqiang Wang,Keqiang Li,Lei Zhang,Dezhao Zhang,Jiaxi Liu,Guizhen Yu,Xiaomin Lian,Lishan Jia,Yugong Luo,Sifa Zheng,Diange Yang,Bo Yang +11 more
TL;DR: In this article, an early warning method for preventing rear-end collision with a rearward vehicle and an early-warning system thereof is proposed, wherein the method comprises the following steps.
Patent
Dynamic detection method for preventing wagon from turning towards one side on bending road and pre-warning apparatus
Jianqiang Wang,Keqiang Li,Dezhao Zhang,Shengbo Li,Xiaomin Lian,Yugong Luo,Sifa Zheng,Diange Yang +7 more
TL;DR: In this paper, a rollover prevention dynamic detection method for a wagon on a curved conduit and a warning device is presented, which includes a steering wheel rotation angle measuring device, a yaw angle velocity measuring device and an electronic control unit consisting of a single chip microcomputer.
Patent
Quick identification method for object vehicle lane changing
Keqiang Li,Lei Guo,Jianqiang Wang,Zhifeng Liu,Yugong Luo,Xiaomin Lian,Diange Yang,Sifa Zheng +7 more
TL;DR: In this article, a method for quickly identifying lanes-change condition of an object vehicle using machine vision and radar data was proposed, which includes using two layers of neural network training to obtain object vehicle weight and setting object vehicle to be dangerous object once calculated confidence coefficient is over confidence threshold.