Bend target identification system and method based on multi-sensor fusion

Abstract

The invention discloses a bend target identification system and method based on multi-sensor fusion, which mainly aims at a problem of front target detection of a vehicle at the bend of expressway. The lane line is divided into a straight line portion of the near view field and a curve portion of the far view field. For information acquisition of a camera, lane line fitting and tracking at the near view field are completed by using Hough transform and Kalman filtering, and curve fitting at the far view field is completed by using a BP neural network. For information acquisition of radar, information of a static object group is extracted, and curve fitting is performed by using the BP neural network. through space-time alignment, the lane line information acquired by vision and the lane line information acquired by the radar are fused to determine a travelable region of the lane where the vehicle is located, and finally a bend target identification algorithm based on the fusion of the camera and the millimeter wave radar is provided by combining the travelable region and the lane line type so as to realize detection for targets at the bend.

Description

technical field [0001] The present invention relates to the field of intelligent terminals, in particular to a multi-sensor fusion-based highway curve target recognition system and method. Background technique [0002] The problem of target recognition and tracking on curves has always been an important topic in the field of environmental perception, which is of great significance to the development of ADAS systems. Taking the ACC system as an example, the existing method mainly automatically adjusts the speed of the cruise vehicle and maintains a safe distance from the vehicle in front of the lane based on millimeter-wave radar information. However, on a curved road section, there are usually multiple target vehicles in front of it. At this time, the system often appears confusion or loss of target vehicles, which may cause rear-end collisions due to abnormal acceleration or deceleration of cruise vehicles. In addition, considering the characteristics of the radar itself, ...

AI Technical Summary

Problems solved by technology

This type of detection method is easily affected by the surrounding environment, and it is difficult to apply to driving environments such as vehicle occlusion and light changes.

The model-based detection method mainly uses a specific parameter model to match lane lines. For example, a hyperbolic model is used for lane line detection. This type of algorithm can calculate lane lines according to the constraints of lane lines when there is a lack of lane lines. Advantages, but the amount of calculation is relatively large, and the lane line model needs to be known in advance, and the accuracy rate will decrease in the environment of changing illumination

Images