An Image-Based Method for Obtaining the Adhesion Coefficient of Urban Road Pavement

Abstract

The invention provides an image-based method for obtaining the adhesion coefficient of urban road pavement. First, a pavement image information database is established, then a pavement image data set is established, a pavement image area extraction network is established and trained, a pavement type recognition network is established and trained, and finally Obtain road adhesion coefficient information; this method can provide road adhesion coefficient information for the development of intelligent driving assistance systems and unmanned driving systems; this method realizes the acquisition of the adhesion coefficient by acquiring the road image ahead, and can realize the early acquisition of road adhesion information; because This method designs a serial method based on the image pavement area extraction network and the pavement identification network, and then simplifies the structure of the pavement identification network, so that the real-time and fast acquisition of the front pavement attachment information can be realized.

Description

technical field [0001] The invention belongs to the technical field of intelligent vehicles, and relates to a method for obtaining a road surface adhesion coefficient, more particularly, to an image-based method for obtaining the road surface adhesion coefficient of an urban road. Background technique [0002] With the development of automobile intelligence, users have higher and higher requirements for the performance of in-vehicle intelligent driving assistance systems and unmanned driving systems, and the performance improvement of most intelligent driving assistance systems depends on the accuracy of dynamic control, high-performance dynamic The design of the control system needs to obtain real-time and accurate road information. The estimator based on the dynamic model can obtain the real-time and accurate estimated value of the road adhesion coefficient, but this dynamic estimation method largely depends on the accuracy of the vehicle model and the tire model. And it n...

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Problems solved by technology

[0002] With the development of automobile intelligence, users have higher and higher performance requirements for vehicle-mounted intelligent driving assistance systems and unmanned driving systems, and the improvement of the performance of most intelligent driving assistance systems depends on the accuracy of dynamics control. The design of the control system needs to obtain real-time and accurate road surface information. The estimator based on the dynamic model can obtain real-time and accurate road adhesion coefficient estimates, but this dynamic estimation method largely depends on the accuracy of the vehicle model and tire model. And it needs to meet certain driving incentive conditions. In addition, the dynamic estimation results reflect the road adhesion coefficient at the contact mark between the tire and the road surface, which has a certain hysteresis, and it is difficult to obtain the predicted value of the road adhesion coefficient in advance.

[0003] At this stage, more and more smart cars are equipped with cameras and other equipment to obtain road information and surrounding vehicle information, which brings new opportunities for the research of road adhesion coefficient recognition methods. Its advantage is that it can perceive the road conditions ahead, so it has A certain predictive ability enables intelligent driving vehicles to adjust the control strategy in advance in the event of a sudden change in the road surface, improving the ability to respond to dangerous conditions, but how to use image sensing information to obtain road surface adhesion coefficient information is still a challenge

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