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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.

Matching low-cost camera and vector HD map is proven to be a practical and effective way of estimating the location and orientation of intelligent vehicles. However, map-based approach is viable only when the landmark observation is adequate and precise. In some areas with sparse and noisy observation, or even non-existent map matching features, the localization results may be unstable. In this paper, we introduce a novel algorithm by fusing visual odometry and vector HD map in a tightly-coupled optimization framework to tackle these problems. Our algorithm exploits the observation of visual feature points and vector HD map landmarks in the sliding window manner and optimize their residuals in a tightly-coupled approach. In this way, the system is more robust against the noisy HD map landmark observations. In addition, our method is able to accurately estimate vehicle pose even when landmarks are sparse. Experiments under two challenging scenarios with noisy and sparse landmark observations show that our method can achieve the Mean Absolute Error (MAE) at 0.1473m and 0.2496m respectively.

Application of human pose estimation in intelligent driving has been gradually popular in recent years, as human pose could be informative in depiction of their actions and intentions. The focus of this paper is pose estimation for vulnerable road users with relatively small amounts of data. We present an improved framework, transferring general human pose estimation algorithms based on convolutional neural network to intelligent vehicle domain. Limb detection is introduced as an auxiliary task to allow explicit appearance-based modeling of spatial relationships among human keypoints. The network for keypoint detection is initialized with the parameters in limb detection task. Besides, the produced limb heatmaps are incorporated with keypoint heatmaps as extra source to gain the final keypoint locations. Concerning practical application, we adapt the widely used pose estimation networks to our proposed framework and evaluate them on the newly introduced TDCB-Pose dataset. The experiment results validate our method, and demonstrate the leverage of limb detection in human pose estimation without abundant data.

Vehicle-road cooperative perception system can greatly improve the perception ability of intelligent vehicles by making use of perception information from road side units (RSU). This paper focuses on the target positioning of static camera for vehicle-road cooperation. A low-cost camera calibration method is proposed to complete the accurate mapping between the image plane and the 3D world space. Precise location of interested targets are achieved by an efficient tracking strategy. Real test scenarios show that our algorithm can effectively locate vehicles, pedestrians, non-motor vehicles and other targets with high accuracy. Our algorithm won the Monocular Static Camera Positioning and Ranging Competition for Autonomous Driving championship in 2019.

High precision localization is a crucial requirement for the autonomous driving system. Traditional positioning methods have some limitations in providing stable and accurate vehicle poses, especially in an urban environment. Herein, we propose a novel self-localizing method using a monocular camera and a 3D compact semantic map. Pre-collected information of the road landmarks is stored in a self-defined map with a minimal amount of data. We recognize landmarks using a deep neural network, followed with a geometric feature extraction process which promotes the measurement accuracy. The vehicle location and posture are estimated by minimizing a self-defined re-projection residual error to evaluate the map-to-image registration, together with a robust association method. We validate the effectiveness of our approach by applying this method to localize a vehicle in an open dataset, achieving the RMS accuracy of 0.345 meter with reduced sensor setup and map storage compared to the state of art approaches. We also evaluate some key steps and discuss the contribution of the subsystems.