摘要：AbstractLane detection and tracking technologies are critical to automated vehicles and the lane departure warning (LDW) feature in advanced driver assistance systems (ADAS). The color difference between lane marks and their background is an important source for drivers to detect lanes. However, most existing lane detection techniques neglect the importance of color features contained within lane marks because the color is subject to lighting variations. The hue, saturation, and value (HSV) color space is commonly used for color identification, as it separates the color, saturation, and brightness information into the H, S, and V channels, respectively. Colors can be identified with specified thresholds in H, S, and V channels, but fixed thresholds can hardly treat the disturbances caused by variation of lighting conditions. This paper leverages the color information of lane marks to perform the lane detection task by adaptively changing the thresholds in the HSV space to handle lighting condition variations. In doing so, the vehicle is assumed to follow a straight lane with a reference steering angle of zero degree under varying lighting conditions. It is hard to model the relationship between the thresholds in the HSV space and the vehicle steering angle in automated driving, so we adopt the model-free control (MFC), which is a data-driven approach and does not require an explicit model for the system. Simulink-QUARC joint offline experiments demonstrate the effectiveness of the proposed real-time HSV threshold adjustment method for better lane detections.
关键词：KeywordsAutonomous vehiclesDetection algorithmsThreshold selectionAdaptive systemsSelf-adjusting systemsModel-free control
