Identification of Weather Conditions Related to Roadside LiDAR Data

Traffic data collection is essential for traffic safety and operations studies and has been recognized as a fundamental component in the development of intelligent transportation systems. In recent years, growing interest is shown by both industrial and academic communities in high-resolution data that can portray traffic operations for all transportation participants such as connected or conventional vehicles, transit buses, and pedestrians. Roadside Light Detection and Ranging (LiDAR) sensors can be deployed to collect such high-resolution traffic data sets. However, LiDAR sensing could be negatively affected in the context of rain, snow, and wind conditions as the collected 3D point clouds of surrounding objects may drift. Weather-caused impacts can lead to difficulties in data processing and even accuracy compromise. Consequently, solutions are desired and sought, focused on the issue that the affected data have been identified through a labor-intensive and time-consuming process. In this research, a methodology is proposed for developing an automatic identification of the LiDAR data sets that are affected by rain, snow, and wind conditions. First, the impacts of rain, snow, and wind are characterized using statistical measures. Detection distance offset (DDO) and Detection distance offset for wind (DDOW) are calculated and investigated, and it shows that rain or snow conditions can be differentiated according to the standard deviation of the DDOs. Snow conditions can be additionally identified using the sum of the DDOs. Unlike rain and snow, wind conditions can be recognized by the differences between the upper and lower boundaries of DDOs, and therefore, a separate analysis is developed. Based upon the multiple analyses developed in the research, an automatic identification process is designed. The thresholds for identifying rain, snow, and wind conditions are set up, respectively. The process is validated using realistic roadside LiDAR data collected at the intersection of McCarran Blvd and Evans Ave in Reno, Nevada. The validation demonstrated that the proposed identification could precisely detect affected data sets in the context of rain, snow, and wind conditions

Traffic Flow Forecast based on Vehicle Count

Real-time traffic predictions have now become a time-being need for efficient traffic management due to the exponentially increasing traffic congestion In this paper a more pragmatic traffic management system is introduced to address traffic congestion especially in countries such as Sri Lanka where there is no proper traffic monitoring database Here the real-time traffic monitoring is performed using TFmini Plus light detection and ranging LiDAR sensor and vehicle count for next five minutes will be predicted by feeding consecutively collected data into the LSTM neural network More than ten separate prediction models were trained varying both window size and the volume of input data delivered to train the models Since the accuracy results of all prediction models were above 70 it demonstrates that this system can produce accurate predictions even if it is trained using less input data collection Similarly the sensor accuracy test also resulted in 89 7 accurac

Drivable Space Datasets Created by Airborne LiDAR and Aerial Imagery

The civil engineering and construction industries are currently using geo browsers such as Google Earth to access satellite and aerial imagery to create and update design drawings for roadway construction, which leads to inaccuracies in the construction phase and in effect, delays the time, and increases the cost of a project. Technological advancements in the civil engineering and construction industries have enabled the design processes to be more efficient and accurate. This research focuses on using the cutting-edge technology of airborne LiDAR and aerial imagery to extract roadway network information from an urban area, which can be used to enhance the durability and serviceability of transportation infrastructure in a complex environment. Research results revealed that the time, cost, and completeness of extracting roadway network information from LiDAR data and aerial imagery are more advantageous than that of digitizing from Google Earth, which involves designing roadway network information based on the designer’s best judgment. Research results also showed that there are still limitations with this approach as it relates to the vi accuracy of detecting the edges of the drivable spaces in an urban environment, mainly due to the failure of the extraction process to distinguish between drivable spaces and adjacent sidewalks or other paved surfaces. Future improvements for this extraction process will need to consider better edge detection methods to improve accuracy in urban environments. The process used for the procedure will be made readily available to the civil engineering and construction industries to enable the users to apply it to their work. Utilizing LiDAR data and aerial imagery to extract drivable space information has advantages over the current industry-adopted method, including being better in time efficiency and cost effectivenes

Automated Vehicle to Vehicle Conflict Analysis at Signalized Intersections by Camera and LiDAR Sensor Fusion

This research presents an approach for safety diagnosis using sensor fusion techniques. This work fuses the outputs of a roadside low-resolution camera and a solid-state LiDAR. For vehicle classification and detection in videos, the YOLO v5 object detection model was utilized. The raw 3D point clouds generated by the LiDAR are processed by two manual steps - ground plane transformation and background segmentation, and two real-time steps - foreground clustering, and bounding box fitting. Taking the generated 2D bounding boxes of both camera and LiDAR, we associate the common bounding box pairs by thresholding on the Euclidean distance threshold of 6 ft between the centroid pairs. We perform weighted measurement update based on the RMSE of each of the sensor\u27s detection compared to manually labeled ground truths. The fused measurements are tracked by using linear constant velocity Kalman Filter. With the generated trajectories, we compute post encroachment time (PET) at pixel level conflicts based on the generated vehicle trajectories. We have proposed a complete bipartite graph matching strategy of vehicle parts along with the conflict angle to obtain conflict types - rear-end, sideswipe, head-on, and angle conflict. A case study on a signalized intersection is presented. The output of the proposed framework performs significantly better than the single sensor-based systems in terms of the number of detections and localization. It is expected that the proposed method can be employed to diagnose road safety problems and inform the required countermeasures

Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

USDOT Grant 69A3551747109CALTRANS TO 049LiDAR is an emerging technology that can provide detailed point-cloud measurements for accurate detection and characterization of objects. The cost of this technology has seen significant reduction in recent years with the scaling of production to meet the demands of wide-ranging applications such as autonomous vehicles, infrastructure inventory and topographic mapping, to name a few. Within the field of infrastructure-based traffic monitoring, recent studies have investigated the use of this sensor for advanced truck classification applications in side-fire orientation, as well as for motorized vehicle, bicycle and pedestrian detection at traffic intersections. This study explored the potential of LiDAR in traffic monitoring applications. These include the investigation in the feasibility of edge-side data processing of real-time LiDAR data, real-time detection of vehicle objects using a state-of-the-art object detection algorithm and the use of LiDAR to estimate microscopic vehicle trajectories within its field of view

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection