Light stream based vehicle motion state estimating method

Abstract

The invention discloses a light stream based vehicle motion state estimating method which is applicable to estimating motion of vehicles running of flat bituminous pavement at low speed in the road traffic environment. The light stream based vehicle motion state estimating method includes mounting a high-precision overlook monocular video camera at the center of a rear axle of a vehicle, and acquiring video camera parameters by means of calibration algorithm; preprocessing acquired image sequence by histogram equalization so as to highlight angular point characteristics of the bituminous pavement, and reducing adverse affection caused by pavement conditions and light variation; detecting the angular point characteristics of the pavement in real time by adopting efficient Harris angular point detection algorithm; performing angular point matching tracking of a front frame and a rear frame according to the Lucas-Kanade light stream algorithm, further optimizing matched angular points by RANSAC (random sample consensus) algorithm and acquiring more accurate light stream information; and finally, restructuring real-time motion parameters of the vehicle such as longitudinal velocity, transverse velocity and side slip angle under a vehicle carrier coordinate system, and accordingly, realizing high-precision vehicle ground motion state estimation.

Description

technical field [0001] The invention relates to a method for estimating the motion state of a vehicle based on optical flow, which belongs to the field of autonomous driving and assisted driving. Background technique [0002] Motion state estimation during vehicle driving is one of the key technologies to realize autonomous driving and assisted driving. Active safety control systems such as braking system (ABS), vehicle electronic stability program (ESP), and four-wheel steering stability control system (4WS) have great practical application value. Whether these active safety control systems can work effectively depends largely on the real-time and accurate acquisition of vehicle state parameters. [0003] At present, in the field of automotive active safety, the estimation methods of the motion state of the vehicle can be mainly divided into three categories. One is indirect measurement, that is, using the output information of existing on-board sensors such as inertial n...

AI Technical Summary

Problems solved by technology

However, the bias of the sensors will gradually accumulate over time, leading to a decrease in the accuracy of the estimation of the vehicle state

The second type is direct measurement, which uses high-precision equipment such as high-precision global positioning system GPS to directly obtain the motion state parameters of the vehicle. The area cannot effectively output information, and the positioning accuracy will also decrease

This method needs to model the vehicle as a whole or even each tire separately. When these models or model parameters are inaccurate, the estimation error is large

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