Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System

Cui, Yuepeng; Xu, Hao; Jian, Wu; Sun, Yuan; Zhao, Junxuan

doi:10.1109/mis.2019.2918115

Cited by 84 publications

(37 citation statements)

References 7 publications

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“…At present, the sensing sensors used in autonomous vehicles mainly include Lidar, millimeter-wave radar, ultrasonic radar, and camera [2][3][4][5]. Lidar can scan and measure by transmitting laser pulses to generate a precise map of road scene topography, which can be used for short-distance and long-distance obstacle detection.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

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Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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“…The advantages of LiDAR sensors and the recently reduced unit prices triggered the innovative application of LiDAR sensors at traffic infrastructures, which can uniquely offer high-resolution high-accuracy trajectory data of all traffic users [41][42][43][44]. The deployment of LiDAR sensors provides the data required by connected-vehicle systems and will reform different areas of traffic engineering and research.…”

Section: Discussionmentioning

confidence: 99%

Data Registration with Ground Points for Roadside LiDAR Sensors

Yue

Jian

et al. 2019

Remote Sensing

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The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.

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“…The scanning rate of the LiDAR is set as 10 Hz. The proposed vehicle detection procedure includes five major steps: background filtering [26], point clustering [27], object classification [28,29], lane identification [30,31] and object association [32]. Vehicle trajectories can be generated with the proposed method.…”

Section: Materials and Preprocessingmentioning

confidence: 99%

“…This means the density of vehicle points on each lane is higher than that of vehicle points near the boundary area of the lane. We applied a revised grid-based clustering (RGBC) developed by the authors' team for lane identification [31]. The RGBC first integrates the vehicle points from multiple frames into one space.…”

Section: Lane Identificationmentioning

confidence: 99%

Real-Time Queue Length Detection with Roadside LiDAR Data

Zhang

et al. 2020

Sensors

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Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.

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Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System

Cited by 84 publications

References 7 publications

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Data Registration with Ground Points for Roadside LiDAR Sensors

Real-Time Queue Length Detection with Roadside LiDAR Data

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