A portable roadside vehicle detection system based on multi-sensing fusion

Zheng, Jianying; Xu, Bin; Wang, Xiang; Fan, Xueliang; Xu, Hao; Sun, Guang

doi:10.1504/ijsnet.2019.097558

Cited by 18 publications

(7 citation statements)

References 25 publications

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“…Figure 1 shows an example of before-and-after background filtering. The previous studies [33][34][35] showed that 3D-DSF can exclude more than 95% of background points from the raw LiDAR data.…”

Section: Background Filteringmentioning

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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Section: Background Filteringmentioning

confidence: 99%

Real-Time Queue Length Detection with Roadside LiDAR Data

Zhang

et al. 2020

Sensors

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“…It is connected to a field computer through a router with Ethernet cables. The real-time traffic signal information can be read from conventional traffic controllers through the national transportation communications for ITS protocol (NTCIP) [16]. New traffic signal controllers directly provide SPaT messages, such as the Trafficware 980ATC TS-2 Type 2 Signal Controller.…”

Section: A Roadside Systemmentioning

confidence: 99%

LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities

Jian

et al. 2019

IEEE Access

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Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user's status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.INDEX TERMS Connected-vehicle, LiDAR data, communication platform, smart cities. II. ARCHITECTURE OF THE LIDAR-ENHANCED CONNECTED INFRASTRUCTURE

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“…In fact, a large amount of research has been done to process LiDAR data under severe weather conditions [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Wojtanowski et al [ 22 ] found that LiDAR is susceptible to adverse weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

Jian

Tian

et al. 2020

Sensors

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Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.

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A portable roadside vehicle detection system based on multi-sensing fusion

Cited by 18 publications

References 25 publications

Real-Time Queue Length Detection with Roadside LiDAR Data

Real-Time Queue Length Detection with Roadside LiDAR Data

LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

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