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
This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.
The roadside deployed light detecting and ranging (LiDAR) has been a solution to fill the data gap for the transition period from the unconnected-vehicles environment to the connected-vehicles system. For the roadside LiDAR system, background filtering is an initial but important step. This paper presented a raster-based method for background filtering with roadside LiDAR data. The proposed method contains four major parts: region of interest (ROI) selection, rasterization, background area detection, and background array generation. The location of the background points was stored in a 3D array. The performance of the raster-based method was tested with the data collected at different scenarios. The comparison to the stateof-the-art also confirmed the robustness of the proposed method. INDEX TERMS Background filtering, roadside LiDAR, connected-vehicles.
The application of infrared camera-related technology is a trending research topic. By reviewing the development of infrared thermal imagers, this paper introduces several main processing technologies of infrared thermal imagers, expounds the image nonuniformity correction, noise removal, and image pseudo color enhancement of infrared thermal imagers, and briefly analyzes some main algorithms used in image processing. The technologies of blind element detection and compensation, temperature measurement, target detection, and tracking of infrared thermal imager are described. By analyzing the main algorithms of infrared temperature measurement, target detection, and tracking, the advantages and disadvantages of these technologies are put forward. At the same time, the development of multi/hyperspectral infrared remote sensing technology and its application are also introduced. The analysis shows that infrared thermal imager processing technology is widely used in many fields, especially in the direction of autonomous driving, and this review helps to expand the reader’s research ideas and research methods.
High frequency of distracted driving behavior is considered a high potential risk for traffic safety. Right-turn drivers' distracted driving behavior can dramatically increase the crash risk considering the complex procedures required by the right-turn movements at intersections. This paper analyzed the influence of several factors including road geometry, environmental factors, and traffic conditions on the occurrence of right-turn drivers' distracted driving activities. The data were collected through the Naturalistic Driving Study (NDS). A total of 581 events including 208 events with distracted driving and 373 events without distracted driving (baseline events) were extracted from the Strategic Highway Research Program 2 (SHRP 2) NDS database. The logistic model and random forest (RF) were applied for regression analysis. It was found that Vehicle Lane Occupied and Traffic Control are significantly correlated to distracted driving behavior in both models. The analysis of odds ratios indicated that dedicated right-turn lane design and adding yield sign at intersections can reduce the probability of having distracted driving behavior. Traffic density and driving time may also play important roles in the occurrence of distracted driving activities. Countermeasures are recommended to reduce distracted driving in this paper. The findings of this paper can help engineers and researchers better understand the dominant factors affecting drivers' distraction. This research can also provide theoretical support for the distraction detection function in the advanced driverassistance systems (ADAS).INDEX TERMS Right-turn driver, distracted driving, driver behavior, traffic safety, naturalistic driving study.
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