An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

Lin, Ciyun; Guo, Yingzhi; Li, Wenjun; Liu, Hui; Wu, Dayong

doi:10.1109/jsen.2021.3057999

Cited by 37 publications

(11 citation statements)

References 36 publications

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“…LiDAR, a modern active visual sensor, has various advantages, such as anti-interference to external light changes, adaptability to complex environments, broad scanning coverage, and rich perception information [15][16][17]. Zhao [18] developed a systematic approach to detect and track pedestrians and vehicles using 16 laser LiDAR sensors, with an average accuracy of 95% in traffic detection, classification, and tracking.…”

Section: B Lidar-based Traffic Information Detectionmentioning

confidence: 99%

“…Zhao [18] developed a systematic approach to detect and track pedestrians and vehicles using 16 laser LiDAR sensors, with an average accuracy of 95% in traffic detection, classification, and tracking. Lin [19] proposed a lane detection algorithm for low-density roadside LiDAR, which can aid in high-precision vehicle positioning in vehicle-to-infrastructure (V2I) cooperation applications within intelligent transportation systems. Liu [20] proposed a novel static background construction method that used the fast Fourier transform (FFT) to classify distant target points and noise points with sparse point clouds to expand the detection range of low-channel roadside LiDAR.…”

Section: B Lidar-based Traffic Information Detectionmentioning

confidence: 99%

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Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

Gong

Lin

et al. 2023

IEEE Access

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This study proposes using connected automatic vehicles (CAVs) as traffic flow detectors to collect and exchange traffic flow data for heterogeneous traffic management and control. The proposed method includes the construction of a mathematical matrix to represent the status of the road section, the use of unsupervised machine learning to evaluate traffic data, and an improved generative adversarial imputation net (GAIN) to evaluate and impute missing traffic data. Next-generation simulation (NGSIM) data are used to verify the accuracy and robustness of the proposed method. One of the primary innovations of this study is the use of GAIN, a deep learning framework based on generative adversarial networks (GANs), to impute missing traffic data. GAIN has been shown to be more robust and stable when handling incomplete heterogeneous data than existing imputation methods. Additionally, this study contributes to the field by proposing the use of CAVs as sensors to detect mixed traffic flow, which could lead to more efficient and accurate traffic management and control. Experimental results demonstrate that the proposed method outperforms existing imputation methods, with a normalized root mean squared error and symmetric mean absolute percentage error of less than 0.2/0.3 and 0.08/0.13 in I-80 and Lankershim Boulevard, respectively. The findings of this study have important implications for the development and implementation of connected and automated vehicle technologies in the field of transportation.INDEX TERMS CAV-based sensor, heterogeneous traffic flow, mixed traffic flow, improved generative adversarial imputation net, NGSIM

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Section: B Lidar-based Traffic Information Detectionmentioning

confidence: 99%

Section: B Lidar-based Traffic Information Detectionmentioning

confidence: 99%

Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

Gong

Lin

et al. 2023

IEEE Access

Self Cite

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“…The importance of LiDAR is recognized by all manufacturers, including Tesla, which currently only uses cameras and radar in Tesla vehicles 1 – 3 Unlike camera-based vision navigation, LiDAR sensors scan the surrounding environment by firing laser beams to obtain the location information of an object. A raw 3D point cloud is humongous and must be filtered.…”

Section: Introductionmentioning

confidence: 99%

Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud

Lin,

Yang,

Gong

et al. 2023

J. Appl. Rem. Sens.

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“…Among them, the laser radar has high detection accuracy and can capture the three-dimensional coordinates of target, distance length, azimuth angle, laser reflection intensity, and laser coding parameters at any time. However, the cost of laser radar is very high, though it has been used to detect lane lines [2,3] and vehicle [4]. Cameras and image sensor are widely used in environment perception because of its low-cost and similarity to that of human visual system.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous vehicle and lane detection via MobileNetV3 in car following scene

Deng

2022

PLoS ONE

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Aiming at vehicle and lane detections on road scene, this paper proposes a vehicle and lane line joint detection method suitable for car following scenes. This method uses the codec structure and multi-task ideas, shares the feature extraction network and feature enhancement and fusion module. Both ASPP (Atrous Spatial Pyramid Pooling) and FPN (Feature Pyramid Networks) are employed to improve the feature extraction ability and real-time of MobileNetV3, the attention mechanism CBAM (Convolutional Block Attention Module) is introduced into YOLOv4, an asymmetric network architecture of "more encoding-less decoding" is designed for semantic pixel-wise segmentation network. The proposed model employed improved MobileNetV3 as feature ex-traction block, and the YOLOv4-CBAM and Asymmetric SegNet as branches to detect vehicles and lane lines, respectively. The model is trained and tested on the BDD100K data set, and is also tested on the KITTI data set and Chongqing road images, and focuses on the detection effect in the car following scene. The experimental results show that the proposed model surpasses the YOLOv4 by a large margin of +1.1 AP50, +0.9 Recall, +0.7 F1 and +0.3 Precision, and surpasses the SegNet by a large margin of +1.2 IoU on BDD100k. At the same time, the detection speed is 1.7 times and 3.2 times of YOLOv4 and SegNet, respectively. It fully proves the feasibility and effectiveness of the improved method.

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An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

Cited by 37 publications

References 36 publications

Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud

Simultaneous vehicle and lane detection via MobileNetV3 in car following scene

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