非可视条件下路面可跨越障碍物特征识别

doi:10.19562/j.chinasae.qcgc.2025.01.007

Abstract

Abstract:

Focusing on the demand of intelligent driving under non-visual conditions， the millimeter-wave radar with the characteristics that can work all day and is less affected by light and weather is used to build a shape and position feature recognition model of crossable obstacles on the road in this paper. Taking the road speed bump as an example， the road obstacle feature perception system based on millimeter wave radar is constructed. The radar antenna plane faces the ground and has a certain angle with the ground to collect road information. The FFT-CZT two-stage processing structure is used to refine the spectrum of radar intermediate frequency data and to obtain the range value with high accuracy. Then， by analyzing the radar point cloud， the shortest target distance measured in each frame is fused to obtain the two-dimensional imaging of the road deceleration zone. Finally， through the analysis of visual data， the geometric model of road deceleration zone is established， and the calculation method of characteristic parameters of deceleration zone is put forward. A real vehicle-testing platform is established to collect data of different angles between millimeter wave radar and the ground from 0 to 90. The average absolute error of the estimated speed bump height at the included angle of 45 is within 4 mm， and the average absolute error of the estimated width is about 21 mm， which verifies the effectiveness of the method proposed in this paper.

Key words: road obstacle detection, millimeter-wave radar, two-dimensional imaging, feature calculation, invisible conditions

Hao Li,Haoze Li. Feature Recognition of Crossable Obstacles on Pavement Under Invisible Conditions[J].Automotive Engineering, 2025, 47(1): 67-76.

Figures/Tables 22

毫米波雷达参数	单位	参数值
起始频率 $f 0$	GHz	77
调频斜率S	MHz/μs	36.017
调频周期 $T m$	μs	110
空闲时间 $T i t$	μs	20
起始采样时间	μs	6
采样率 $f s$	kSps	2 560
采样点数N		256
天线通道数P		2×4
每帧chirp数M		64

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宽度估计值	数值/mm	长度估计值	数值/mm
H₁	43.9	L₁	806.2
H₂	40.4	L₂	889.6
H₃	42.7	L₃	778.4

宽度估计值	数值/mm	长度估计值	数值/mm
H₁	44.4	L₁	651.5
H₂	42.5	L₂	661.9
H₃	42.3	L₃	607.3

宽度估计值	数值/mm	长度估计值	数值/mm
H₁	43.9	L₁	838.9
H₂	45.9	L₂	892.2
H₃	36.7	L₃	736.0

宽度估计值	数值/mm	长度估计值	数值/mm
H₁	40.3	L₁	716.5
H₂	40.7	L₂	752.7
H₃	42.1	L₃	749.7

宽度估计值	数值/mm	长度估计值	数值/mm
H₁	40.3	L₁	781.2
H₂	40.7	L₂	780.5
H₃	42.1	L₃	777.5

Feature Recognition of Crossable Obstacles on Pavement Under Invisible Conditions

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References 29

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