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This article proposes an improved lane detection and tracking method for autonomous vehicle applications. In real applications, when the pose and position of the camera are changed, parameters and thresholds in the algorithms need fine adjustment. In order to improve adaptability to different perspective conditions, a width-adaptive lane detection method is proposed. As a useful reference to reduce noises, vanishing point is widely applied in lane detection studies. However, vanishing point detection based on original image consumes many calculation resources. In order to improve the calculation efficiency for real-time applications, we proposed a simplified vanishing point detection method. In the feature extraction step, a scan-line method is applied to detect lane ridge features, the width threshold of which is set automatically based on lane tracking. With clustering, validating, and model fitting, lane candidates are obtained from the basic ridge features. A lane-voted vanishing point is obtained by the simplified grid-based method, then applied to filter out noises. Finally, a multi-lane tracking Kalman filter is applied, the confirmed lines of which also provide adaptive width threshold for ridge feature extraction. Real-road experimental results based on our intelligent vehicle testbed proved the validity and robustness of the proposed method.

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Driving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types of driving space are analyzed. The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.

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