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

Abstract

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

Vehicle Tracking and Speed Estimation From Roadside Lidar

“…There are several classifiers commonly used in lidar-based object detection. For example: Support Vector Machine (SVM) was trained as the classifier and four major different kernel types were compared in [9]; the performance of different classifiers, including naive Bayes, k-nearest neighbor, SVM, and random forest (RF), were compared in [11]; and SVM using the Gaussian radial basis function and RF were experimented with in [16] to detect vehicles from point clouds.…”

“…In the final stage of vehicle tracking, the commonly used methods are Global Nearest Neighbor (GNN) [8] and Kalman Filtering (KF) [10]. It is also noteworthy that these two algorithms are used simultaneously in some works [9], [11], [12]. Normally, the spatial center of a cluster is identified as the location of the vehicle being tracked.…”

“…Nevertheless, this center shifts, frame by frame, due to the incompleteness of the scanned vehicle from the roadside lidar system. There are several studies that have taken this shift into consideration [11]- [13] , and these have largely adopted the same strategy whereby the point in the vehicle cluster closest to the lidar instrument is selected as the tracking point. This means that when the vehicle is approaching the sensor, the front corner of the cluster acts as the tracking point, and when the vehicle is leaving the sensor, the back corner of the cluster is the tracking point.…”

A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data

“…For the redundant filtering algorithm, we want to get the highest possible data compression rate with the smallest possible error rate. Therefore, we use the following formula to express filtering performance [26]:…”

Conflicts of Automated Driving With Conventional Traffic Infrastructure

“…[6] The sensors' information (LiDAR) help to get better and safer connected transportation. [20] But some researches deal with the issue that is complying with AV rules (influenced by the traffic signs, signals and infrastructure) do not always benefit safe transport on the public roads. [26] Some researchers said, at potential infrastructural changes to a road section for easier future integration of autonomous vehicles.…”