Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

2019 ◽  
Vol 2673 (9) ◽  
pp. 62-71 ◽  
Author(s):  
Zhenyao Zhang ◽  
Jianying Zheng ◽  
Hao Xu ◽  
Xiang Wang
Keyword(s):  
High Resolution ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Traffic Data ◽  
The Road ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Vehicle Detection And Tracking ◽  
On The Road

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

Study on the Highway Speed Limit at the Days of Snow and Ice

2012 ◽  
Vol 226-228 ◽  
pp. 2362-2365
Author(s):  
Jun De Liu ◽  
Tian Tian Peng ◽  
Jing Zhuang ◽  
Ya Juan Deng
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Speed Limit ◽  
Practical Application ◽  
Highway Traffic ◽  
The Road ◽  
Ice Conditions ◽  
On The Road ◽  
Rate Limiting ◽  
Snow And Ice

In order to prevent traffic accidents caused by bad speed snow day weather and achieve the purpose of protection of highway traffic safety. Through the analysis of ice conditions, we identified of the rate-limiting model in the snow, snow, snow icing conditions and curve sections, we applied to the model and the calculation of mathematical solution and come to the conclusion of the speed limit which is recommended values at the days of snow and ice disaster conditions. In the practical application, it is convenient to get the speed limit under the necessary conditions through look-up the table of this article in order to ensure the safety of traffic on the road.

scholarly journals Road Traffic Safety Analysis Of Different Junction Types On The State Roads

2021 ◽  
Vol 1202 (1) ◽  
pp. 012034
Author(s):  
Valentina Amare ◽  
Juris Smirnovs
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accidents ◽  
The Road ◽  
Road Traffic Safety ◽  
Road Capacity ◽  
On The Road ◽  
The Impact

Abstract The highest number of road accidents occurs at junctions. One of the aims of traffic organisation is to improve traffic safety in these areas. Based on a variety of indices – road capacity, points of conflict, number, and severity of road traffic accidents – different alternatives for junctions are evaluated. However, the road network has many junctions and roads serve to travel from point "A" to point "B" at a given time. Therefore, one of the most important tasks when addressing the issue of road safety is to find a rational way of improving the safety without losing the importance of the road. The aim of this paper is to analyse the impact of different junctions on the road network and basing on actual data develop a method for the evaluation of different types of junctions with respect to road class.

Gender Differences and Aggressive Driving Behavior: A Factor Analytic Study

2007 ◽  
Vol 51 (18) ◽  
pp. 1283-1286 ◽  
Author(s):  
Mustapha Mouloua ◽  
J. Christopher Brill ◽  
Edwin Shirkey
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
The Road ◽  
Factor Analytic ◽  
Components Analysis ◽  
On The Road

Aggressive driving behavior can be manifested in a wide variety of unsafe driving practices such as tailgating, honking, obscene and rude gestures, flashing high beams at slower traffic, and speeding. According the National Highway Traffic Safety Administration 2000 report, aggressive driving was a major cause of traffic accidents and injury. The present study was designed to systematically examine 5 previously developed scales related to aggressive driving behavior using a factor analytic approach. A sample of 253 students were administered these five questionnaires and the data were coded and statistically analyzed using a principal components analysis with Varimax rotation on the 81 items of the five combined scales. Nineteen components accounting for 67.4% of the variance were retained. Component scores were computed for the 19 components and then correlated with gender. Three significant ( p < .05) positive r's were found between gender; factors 11 (bright lights action), 12 (delaying action), and 19 (driving drunk). Males in the sample reported performing these actions more than females. There was one negative r between gender and factor 4 (considerate thoughts), suggesting that females reported more pleasant thoughts than males when angered or annoyed on the road.

scholarly journals Improvement in Target Range Estimation and the Range Resolution Using Drone

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1136 ◽  
Author(s):  
Kwan Hyeong Lee
Keyword(s):  
Detection Rate ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Target Range ◽  
Vehicle Speed ◽  
Moving Vehicle ◽  
Range Resolution ◽  
The Road ◽  
Moving Vehicles ◽  
On The Road

This study measured the speed of a moving vehicle in multiple lanes using a drone. The existing methods for measuring a vehicle’s speed while driving on the road measure the speed of moving automobiles by means of a sensor that is mounted on a structure. In another method, a person measures the speed of a vehicle at the edge of a road using a speed-measuring tool. The existing method for measuring a vehicle’s speed requires the installation of a gentry-structure; however, this produces a high risk for traffic accidents, which makes it impossible to measure a vehicle’s speed in multiple lanes at once. In this paper, a method that used a drone to measure the speed of moving vehicles in multiple lanes was proposed. The suggested method consisted of two LiDAR sets mounted on the drone, with each LiDAR sensor set measuring the speed of vehicles moving in one lane; that is, estimating the speed of moving vehicles in multiple lanes was possible by moving the drone over the road. The proposed method’s performance was compared with that of existing equipment in order to measure the speed of moving vehicles using the manufactured drone. The results of the experiment, in which the speed of moving vehicles was measured, showed that the Root Mean Square Error (RMSE) of the first lane and the second lane was 3.30 km/h and 2.27 km/h, respectively. The vehicle detection rate was 100% in the first lane. In the second lane, the vehicle detection rate was 94.12%, but the vehicle was not detected twice in the experiment. The average vehicle detection rate is 97.06%. Compared with the existing measurement system, the multi-lane moving vehicle speed measurement method that used the drone developed in this study reduced the risk of accidents, increased the convenience of movement, and measured the speed of vehicles moving in multiple lanes using a drone. In addition, it was more efficient than current measurement systems because it allowed an accurate measurement of speed in bad environmental conditions.

Study on Vehicles Speed Discreteness on the Road of Urban-Rural Ecotone

2012 ◽  
Vol 253-255 ◽  
pp. 1967-1970
Author(s):  
Yan Li
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Simulation Software ◽  
The Road ◽  
Road Traffic Safety ◽  
Geographical Unit ◽  
Urban Rural ◽  
On The Road ◽  
The City

Urban-rural ecotone is separate geographical unit, and it is in the parallel connecting area of the city and countryside. With the rapid economic development, traffic accidents in the area showing a significant increase in the trend. Because of vehicles in urban-rural ecotone of many types, traffic is more complex, speed variations and high frequency of speed changes, a greater impact on such sections of road traffic safety. Therefore, this article by the typical section of the survey, combined with the traffic simulation software, using grey relation entropy analysis method to analyze speed discrete of the urban-rural ecotone road, so as to clear the characteristics of various types of vehicles speed.

Analysis of Vehicle–Pedestrian Interactive Behaviors near Unsignalized Crosswalk

2021 ◽  
pp. 036119812199906
Author(s):  
Byeongjoon Noh ◽  
Dongho Ka ◽  
David Lee ◽  
Hwasoo Yeo
Keyword(s):  
South Korea ◽  
Traffic Accidents ◽  
Road Traffic ◽  
The Road ◽  
Video Footage ◽  
Premature Deaths ◽  
Vehicle Velocity ◽  
Behavioral Features ◽  
Velocity Changes ◽  
On The Road

Road traffic accidents are a leading cause of premature deaths and globally pose a severe threat to human lives. In particular, pedestrians crossing the road present a major cause of vehicle–pedestrian accidents in South Korea, but we lack dense behavioral data to understand the risk they face. This paper proposes a new analytical system for potential pedestrian risk scenes based on video footage obtained by road security cameras already deployed at unsignalized crosswalks. The system can automatically extract the behavioral features of vehicles and pedestrians, affecting the likelihood of potentially dangerous situations after detecting them in individual objects. With these features, we can analyze the movement patterns of vehicles and pedestrians at individual sites, and understand where potential traffic risk scenes occur frequently. Experiments were conducted on four selected behavioral features: vehicle velocity, pedestrian position, vehicle–pedestrian distance, and vehicle–crosswalk distance. Then, to show how they can be useful for monitoring the traffic behaviors on the road, the features are visualized and interpreted to show how they may or may not contribute to potential pedestrian risks at these crosswalks: (i) by analyzing vehicle velocity changes near the crosswalk when there are no pedestrians present; and (ii) analyzing vehicle velocities by vehicle–pedestrian distances when pedestrians are on the crosswalk. The feasibility of the proposed system is validated by applying the system to multiple unsignalized crosswalks in Osan city, South Korea.

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