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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yan, Min Wang, Junzheng Li, Jing Zhang, Ke Yang, Zimu Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China

ISBN: (纸本)9781728124858

detection of traversable road area is one of the key tasks for unmanned platforms to achieve autonomous navigation. We propose a road detection method using lidar data based on plane assumption and graph model in this paper. Two detection algorithms for different speeds are provided to ensure the detection accuracy and to meet the real-time requirements, respectively. First, the point cloud is projected into a real image or an imaginary image for triangulation. The road points are then extended outward from the near platform according to the plane assumption. Then the dual Voronoi diagram of the triangulation is filled according to the attribute of the central element node, and the upper part of the horizon is removed. Finally, image post-processing yields the final detection results. This method perfectly combines the classification of points and the determination of road areas and uses the mature image algorithms in the processing of point cloud data. Experimental tests have been carried out on the public KITTI-road benchmark, obtaining positive results.

关键词： road detection triangulation plane fitting lidar

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Fast road detection by CNN-Based Camera-Lidar Fusion and Spherical Coordinate Transformation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第9期22卷 5802-5810页

作者： Lee, Jae-Seol Park, Tae-Hyoung Chungbuk Natl Univ Dept Control & Robot Engn Cheongju 28644 South Korea Chungbuk Natl Univ Sch Elect Engn Cheongju 28644 South Korea

We propose a new camera-lidar fusion method for road detection where the spherical coordinate transformation is introduced to decrease the gap between the point cloud of 3D lidar data. The camera's color data and the 3D lidar's height data are transformed into the same spherical coordinate, and then input to the convolution neural network for segmentation. Faster segmentation is possible due to the reduced size of input data. To increase the detection accuracy, this modified SegNet expands the receptive field of the network. Using the KITTI dataset, we present the experimental results to show the usefulness of the proposed method.

关键词： Laser radar Cameras Sensor fusion Autonomous vehicles Image segmentation Convolutional neural networks road detection lidar and camera fusion segmentation convolution neural network spherical coordinate transformation autonomous vehicles

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Toward real-time road detection for autonomous vehicles

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JOURNAL OF ELECTRONIC IMAGING 2020年第4期29卷 43022-43022页

作者： Yazid, Lachachi M. Mohamed, Ouslim Smail, Niar Abdelmalik, Taleb-Ahmed Univ Sci & Technol Oran Mohamed Boudiaf LMSE Lab Oran Algeria Univ Polytech Hauts de France LAMIH Lab Valenciennes France

road detection is a vital task for autonomous vehicles, as it has a direct link to passengers' safety. Given its importance, researchers aimed to improve its accuracy and robustness. We look at the task from a holistic point of view, where we aim to balance computation and accuracy. A multimodal road detection pipeline is proposed, which fuses the camera image with the preprocessed LIDAR input. First, the LIDAR input is preprocessed using three-dimensional models inspired from computer graphics to generate image-like representations. Then, the preprocessed LIDAR input is combined with the camera image using a fusion module named inputs cross-fusion module, to reduce the computation amount required by other fusion strategies. To prevent the accuracy loss caused by the computation gain, we introduce the surface normal information to add distinctiveness. Furthermore, we propose a cost/benefit metric to evaluate the trade-off between computation cost and accuracy of road detection approaches. Several tests were conducted using the KITTI road detection benchmark based on deep convolutional neural networks, the obtained results were considered very satisfactory. In particular, the robustness of the proposed approach resulted in accuracies higher than 95% on different road types, comparable to those of the state-of-the-art techniques. In addition to marginally reducing the inference time of the used DCNN on images with a resolution of 1248 x 352 pixels to 130 ms using an NVIDIA GTX-1080TI. (C) 2020 SPIE and IS&T

关键词： road detection LIDAR camera fusion surface normal autonomous vehicle

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Recursive drivable road detection with shadows based on two-camera systems

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MACHINE VISION AND APPLICATIONS 2017年第5-6期28卷 509-523页

作者： Jia, Bingxi Chen, Jian Zhang, Kaixiang Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou Zhejiang Peoples R China

This paper concerns the road reconstruction problem of on-road vehicles with shadows. To deal with the effects of shadows, images are transformed to the proposed illuminant invariant color space and are fused with raw images to obtain more details in both dark and bright regions. Then, the road region is reconstructed from a geometry point of view. Based on the two-view geometric model, the scene can be described by the projective parallax information with respect to a reference plane, which is the grounding plane of the vehicle in this paper. The road reconstruction is performed row-by-row recursively. For each row, a row-wise image registration method is used to estimate the parallax information, based on which height information with respect to the reference plane can be calculated. Based on the distributions of height information and image intensity, the road region is detected and is used to direct the image registration of the next row. This process stops when the road region is small enough and the entire road region is obtained. The proposed approach is general in the sense that it works with shadows for different road types and camera configurations. Experimental results are provided to show the effectiveness and robustness of the proposed method.

关键词： road detection Illuminant invariant Two-camera system

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Hybrid conditional random field based camera-LIDAR fusion for road detection

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INFORMATION SCIENCES 2018年 432卷 543-558页

作者： Xiao, Liang Wang, Ruili Dai, Bin Fang, Yuqiang Liu, Daxue Wu, Tao Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China Massey Univ Inst Nat & Math Sci Auckland New Zealand

road detection is one of the key challenges for autonomous vehicles. Two kinds of sensors are commonly used for road detection: cameras and LIDARs. However, each of them suffers from some inherent drawbacks. Thus, sensor fusion is commonly used to combine the merits of these two kinds of sensors. Nevertheless, current sensor fusion methods are dominated by either cameras or LIDARs rather than making the best of both. In this paper, we extend the conditional random field (CRF) model and propose a novel hybrid CRF model to fuse the information from camera and LIDAR. After aligning the LIDAR points and pixels, we take the labels (either road or background) of the pixels and LIDAR points as random variables and infer the labels via minimization of a hybrid energy function. Boosted decision tree classifiers are learned to predict the unary potentials of both the pixels and LIDAR points. The pairwise potentials in the hybrid model encode (i) the contextual consistency in the image, (ii) the contextual consistency in the point cloud, and (iii) the cross-modal consistency between the aligned pixels and LIDAR points. This model integrates the information from the two sensors in a probabilistic way and makes good use of both sensors. The hybrid CRF model can be optimized efficiently with graph cuts to get road areas. Extensive experiments have been conducted on the KITTI-road benchmark dataset and the experimental results show that the proposed method outperforms the current methods. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Sensor fusion road detection Condition random field Boosted decision tree

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GAMSNet: Globally aware road detection network with multi-scale residual learning

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 175卷 340-352页

作者： Lu, Xiaoyan Zhong, Yanfei Zheng, Zhuo Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Nat Resources Remote Sens Wuhan 430079 Peoples R China

road detection from very high-resolution (VHR) remote sensing imagery is of great importance in a broad array of applications. However, the most advanced deep learning based methods often produce fragmented road segments, due to the complex backgrounds of the images, such as the occlusions and shadows caused by trees and buildings, or the surrounding objects with similar textures. In this research, the characteristics of the existing deep learning based road detection methods are analyzed and effective road detection methods are explored, and we show that capturing long-range dependencies can significantly improve the road recognition performance. The novel globally aware road detection network with multi-scale residual learning (GAMS-Net) is proposed, in which multi-scale residual learning is applied to obtain multi-scale features and expand the receptive field, and the global awareness operation is used to capture the spatial context dependencies and inter-channel dependencies. Through capturing useful information over long distances, GAMS-Net can significantly improve the road recognition performance. The advantages of the proposed approach are validated using the public Deep-Globe road dataset and large-scale images, and the experimental results confirm the superiority of the proposed method.

关键词： road detection Remote sensing Global-aware Multi-scale Deep learning

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Scene-Adaptive Off-road detection Using a Monocular Camera

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2018年第1期19卷 242-253页

作者： Mei, Jilin Yu, Yufeng Zhao, Huijing Zha, Hongbin Peking Univ Key Lab Machine Percept MOE Beijing 100871 Peoples R China Peking Univ Key Lab Machine Percept Beijing 100871 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept Beijing 100871 Peoples R China Peking Univ Ctr Informat Sci Key Lab Machine Percept Beijing 100871 Peoples R China Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

This paper studies vision-based road detection for a robot's path following in off-road environments. We define the problem as detecting the region in front of the robot that is mechanically traversable (i.e., mechanical traversability), that is apt to be chosen by a human to drive through (i.e., human selection), and that extends for a distance to show the road's direction, shape, or even network of the intersection ahead (i.e., far-field capability). An algorithm framework is designed that contains two parts: inference and learning. In inference, the problem is formulated as a consecutive road type classification and road region segmentation to address the diversity of terrain surfaces. In model learning, the robot is first driven by a human being, with image samples on the track of the robot being collected that meet the prerequisites of both mechanical traversability and human selection. Evaluation measures are defined to examine the three requirements of mechanical traversability, human selection, and far-field capability. The performances of the above aspects are demonstrated on a data set using LiDAR, track and manual references, which will be released together with this publication.

关键词： road detection off-road environments vision-based method

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Enabling road detection Tasks via Collaborative Smart Vehicles

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IEEE INTERNET OF THINGS JOURNAL 2022年第6期9卷 4269-4279页

作者： Yuan, Hao Guo, Deke Liao, Hanlong Wu, Rui Li, Jiangfan Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Vehicular cloud computing (VCC) has been widely utilized to enhance traffic management and road safety both in the static and dynamic scenarios. In VCC, the onboard resources of involved smart vehicles (SVs) would be integrated as a resource pool and collaboratively accommodate various computation tasks. In this system, SVs in a section of the road can be grouped as a vehicular cloud to complete the detection job, e.g., collecting and preprocessing the road data. Then, an SV will be selected to aggregate all SVs' detection results in the road section and upload the results to the remote monitoring platform via base stations (BSs). However, due to the high dynamics of vehicular mobility and the poor coverage of BSs in some areas, this vision is challenging to achieve. In this article, we propose a collaborative road detection system and schedule the detection task to different SVs in the road section. Specifically, 1) we model the collaborative detection process and formulate it as a task scheduling problem to minimize the response time, which is NP-hard. An adaptive location-aware scheduling scheme is proposed for task scheduling and 2) as there are road sections without coverage of any BS, therefore we have to utilize the SVs on the opposite lane to transmit the aggregation result in these road sections. Accordingly, an uploading strategy is proposed to decide to upload the feedback through SVs on the opposite lane or in the coverage area of the next BS. Extensive experiments show that our scheme can significantly reduce the response time overall and is close to the optimal solution.

关键词： roads Task analysis Monitoring Computational modeling Processor scheduling Scheduling Cloud computing road detection smart vehicle task scheduling vehicular cloud computing

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Deep representation learning for road detection using Siamese network

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第17期78卷 24269-24283页

作者： Liu, Huafeng Han, Xiaofeng Li, Xiangrui Yao, Yazhou Huang, Pu Tang, Zhenmin Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur Intelligent Proc Nanjing 210023 Jiangsu Peoples R China

Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI road demonstrate the effectiveness of our proposed approach.

关键词： road detection Siamese network Data fusion Deep learning

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An End-to-End Multi-Task Learning Model for Drivable road detection via Edge Refinement and Geometric Deformation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8641-8651页

作者： Li, Keqiang Xiong, Hui Yu, Dameng Liu, Jinxin Guo, Yu'ang Wang, Jianqiang Tsinghua Univ Sch Vehicle & Mobil State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Huawei Technol Co Ltd Shanghai 201206 Peoples R China Beihang Univ Sch Transportat Sci & Engn Key Lab Autonomous Transportat Technol Special Ve Beijing 100084 Peoples R China

This paper presents a road detection method for autonomous driving based on an end-to-end neural network model. Our method takes advantage of both the characteristics of road boundary and multi-task learning of a deep convolutional network. By reassigning the label and rebalancing the loss of road pixels, we focus on the learning of hard examples on the boundary to refine its performance. Then, a road geometric transformation-based data augmentation method is proposed to enable the network model to be robust under traffic scenes. Based on these two novel methods, a unified architecture consisting of a shared deep residual encoder network and multi-branch decoder sub-networks is integrated. It adopts road scene classification as a supervised learning task to realize road segmentation and scene classification simultaneously. Experiments demonstrate that the proposed method has achieved the highest MaxF value in most road scenes. Both qualitative and quantitative evaluations on the KITTI-road benchmark demonstrate our superior performance.

关键词： Intelligent vehicles road detection image segmentation deep convolutional neural network

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