Aiming at the problem of measuring the height of surrounding obstacles using the monocular fisheye camera of AVM system in parking scenarios, a method of measuring height through inverse perspective transformation and multiple calibration is proposed and verified by simulation experiments. First, the distortion correction of the fish-eye camera and the calculation of the homography transformation matrix are performed to complete the inverse perspective transformation, and the transformation relationship between the original camera image and the vertical plane image is obtained. Secondly, the YOLO-v4 target detection algorithm is used to identify obstacles and return the pixel height of obstacles. Then, according to the calculation formula of monocular camera height measurement and the relationship between pixel distance and object distance established by calibration, the method of measuring obstacle height is determined. Finally, the effectiveness of the method is verified by simulation experiments.
KEYWORDS: Roads, Information theory, Autonomous driving, Autonomous vehicles, Unmanned vehicles, Scene classification, Matrices, Design and modelling, Data modeling, Analytics
With the continuous development of automotive driving automation, scenario-based automated driving test evaluation methods have become an industry consensus. However, in terms of scenarios, the industry still relies on the subjective experience of experts to formulate evaluation plans, lacking scientific and quantitative evaluation methods, resulting in the problems of limited scenario coverage and low testing efficiency, which affect the mass production process of products. Therefore, this paper proposes a scene complexity evaluation method based on analytic hierarchy process (AHP) and information entropy theory, which realizes the automatic quantitative evaluation of test scene complexity and makes up for the lack of theoretical research on industry test scene. Finally, the method proposed in this paper quantitatively analyzes the complexity of typical scenarios in the Autonomous Driving Evaluation Project of CATARC, summarizes the key factors affecting the complexity of the scenario, and verifies the feasibility and effectiveness of the method.
With the rapid development of intelligent driving technology, autonomous vehicle following system has been widely used. In this paper, the technical characteristics, test methods and standards of the current autonomous following system are analyzed. Combining with the capability and ODD of the autonomous following system of vehicles on sale, a vehicle test and evaluation method of the autonomous following system is proposed, and a reasonable and feasible test technical scheme is put forward. The proposed test evaluation method and test technical scheme were verified by using automatic driving robot, GST and SCT and other test equipment. The test results show that there is space for further refinement in the evaluation of comfort, and the evaluation of safety can represent the safety of the tested vehicle in a relatively comprehensive way
China 's automatic driving main-routes logistics industry is ushering in a new window of development. Enterprises in different fields are closely cooperating in a comprehensive layout, which makes industry ecology is constantly enriched. However, the development problems such as bottleneck of L4 autonomous driving technology, imperfect development system and imperfect laws and regulations are gradually emerging. The application background, application status and development trend of automatic driving main-routes logistics systematically were sorted out in this paper. From the aspects of automatic driving technology, market development system, industry policies and regulations, targeted suggestions for the problems exposed in the development of the industry were proposed. In the future, L3 trucks will continue to promote algorithm iterations. The government and all parties in the industry will improve standards, regulations and supply systems. At the same time, under continuous pressure from foreign brands, automatic driving main-routes logistics in China will continue to develop.
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