Machine Vision-Based Method for Estimating Lateral Slope of Structured Roads

Abstract

Most of the studies on vehicle control and stability are based on cases of known-road lateral slope, while there are few studies on road lateral-slope estimation. In order to provide reliable information on slope parameters for subsequent studies, this paper provides a method of structured-road lateral-slope estimation based on machine vision. The relationship between the road lateral slope and the tangent slope of the lane line can be found out according to the image-perspective principle; then, the coordinates of the pre-scan point are obtained by the lane line, and the tangent slope of the lane line is used to obtain a more accurate estimation of the road lateral slope. In the implementation process, the lane-line feature information in front of the vehicle is obtained according to machine vision, the lane-line function is fitted according to an SCNN (Spatial CNN) algorithm, then the lateral slope is calculated by using the estimation formula mentioned above. Finally, the road model and vehicle model are established by Prescan software for off-line simulation. The simulation results verify the effectiveness and accuracy of the method.

Development and Functional Validation Method of the Scenario-in-the-Loop Simulation Control Model Using Co-Simulation Techniques

“…In the simulation, all participants can be adjusted and handled on the same level, and dynamic calculations are available by every vehicle on demand. The software can be effectively used in ADASs and autonomous driving function-related simulations [42,43]. The SciL concept can also be realized with widely used simulation tools like the previously mentioned software, providing a wider range of possible applications.…”

“…In the simulation, all participants can be adjusted and handled on the same level, and dynamic calculations are available by every vehicle on demand. The software can be effectively used in ADASs and autonomous driving function-related simulations [42,43].…”

Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things

“…If all links of incoming materials, processing, assembly, packaging and logistics are highly automated, the equipment on the production line needs accurate operation, quick response and close networking cooperation, where the functional assistance of machine vision technology plays a key role [9][10][11][12][13]. Machine vision can reliably identify various targets in the production process, and is helpful for the control system to make accurate and rapid production decisions [14][15][16][17][18][19][20][21]. Different from the recognition and detection of some known features, it is difficult to realize defect detection in machine vision applications.…”