This article presents a novel path planning algorithm for autonomous land vehicles. There are four main contributions: Firstly, an evaluation standard is introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Secondly, a guideline generated by human or global planning is employed to develop the heuristic function to overcome the shortcoming of traditional A-Star algorithms. Thirdly, for improving the obstacle avoidance performance, key points around the obstacle are employed, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Fourth, a novel variable-step based A-Star algorithm is also introduced to reduce the computing time of the proposed algorithm. Compared with the state-of-the-art techniques, experimental results show that the performance of the proposed algorithm is robust and stable.

Negative obstacles for field autonomous land vehicles (ALVs) refer to ditches, pits, or terrain with a negative slope, which will bring risks to vehicles in travel. This paper presents a feature fusion based algorithm (FFA) for negative obstacle detection with LiDAR sensors. The main contributions of this paper are fourfold: (1) A novel three‐dimensional (3‐D) LiDAR setup is presented. With this setup, the blind area around the vehicle is greatly reduced, and the density of LiDAR data is greatly improved, which are critical for ALVs. (2) On the basis of the proposed setup, a mathematical model of the point distribution of a single scan line is deduced, which is used to generate ideal scan lines. (3) With the mathematical model, an adaptive matching filter based algorithm (AMFA) is presented to implement negative obstacle detection. Features of simulated obstacles in each scan line are employed to detect the real negative obstacles. They are supposed to match with features of the potential real obstacles. (4) Grounded on AMFA algorithm, a feature fusion based algorithm is proposed. FFA algorithm fuses all the features generated by different LiDARs or captured at different frames. Bayesian rule is adopted to estimate the weight of each feature. Experimental results show that the performance of the proposed algorithm is robust and stable. Compared with the state‐of‐the‐art techniques, the detection range is improved by 20%, and the computing time is reduced by an order of two magnitudes. The proposed algorithm had been successfully applied on two ALVs, which won the champion and the runner‐up in the “Overcome Danger 2014” ground unmanned vehicle challenge of China.

This paper presents a camera-based lane departure warning system implemented on a field programmable gate array (FPGA) device. The system is used as a driver assistance system, which effectively prevents accidents given that it is endowed with the advantages of FPGA technology, including high performance for digital image processing applications, compactness, and low cost. The main contributions of this work are threefold. (1) An improved vanishing point-based steerable filter is introduced and implemented on an FPGA device. Using the vanishing point to guide the orientation at each pixel, this algorithm works well in complex environments. (2) An improved vanishing point-based parallel Hough transform is proposed. Unlike the traditional Hough transform, our improved version moves the coordinate origin to the estimated vanishing point to reduce storage requirements and enhance detection capability. (3) A prototype based on the FPGA is developed. With improvements in the vanishing point-based steerable filter and vanishing point-based parallel Hough transform, the prototype can be used in complex weather and lighting conditions. Experiments conducted on an evaluation platform and on actual roads illustrate the effective performance of the proposed system.

Unstructured road detection is a key step in an unmanned guided vehicle (UGV) system for road following. However, current vision‐based unstructured road detection algorithms are usually affected by continuously changing backgrounds, different road types (shape, colour), variable lighting conditions and weather conditions. Therefore, a confidence map of road distribution, one of contextual information cues, is theoretically analysed and experimentally generated to help detect unstructured roads. Two traditional algorithms, support vector machine (SVM) and k‐nearest neighbour (KNN), are carried out to verify the helpfulness of the proposed confidence map. Following this, a novel algorithm, which combines SVM, KNN and the confidence map under a Bayesian framework, is proposed to improve the overall performance of the unstructured road detections. The proposed algorithm has been evaluated using different types of unstructured roads and the experimental results show its effectiveness

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Light Detection And Ranging (LiDAR) has been widely employed in Unmanned Ground Vehicle (UGV) for autonomous navigation and object detection. In this paper, an efficient extrinsic parameter calibration approach, which is based on a pair of orthogonal normal vectors, is presented for an arbitrary equipped 3-D LiDAR. With the proposed approach, the whole calibration process can be easily and efficiently implemented in outdoor urban environment and no calibration equipment is required. The main advantages of this approach are twofold: (1) compared with traditional ways, the proposed approach employs an orthogonal normal vector pair, which is generated by ground plane and vertical wall in urban environment, so calibration equipments are not required anymore; (2) the normal vector is estimated from the point cloud data on a surface, thus a quite robust and accuracy estimation can be obtained. Experiments illustrate the effective and efficient performance of the proposed approach, compared with the state of the art.

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