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.

A study on Private Car Accidents

2021 ◽  
Vol 11 (1) ◽  
pp. 10-24
Author(s):  
Abdulmajeed Alamri ◽  
Tarek M. Esmael ◽  
Sami Fawzy ◽  
Hany Hosny ◽  
Saleh Attawi ◽  
...  
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Economic Losses ◽  
Moving Vehicle ◽  
The Road ◽  
Lost Productivity ◽  
On The Road

In this study, road traffic injury (RTI) was defined as any injury resulting from a road traffic accident irrespective of severity and outcome. Road traffic accident (RTA) was defined as any crash on the road involving at least one moving vehicle, irrespective of it resulting in an injury. This could include collision with a vehicle or any non`moving object while driving/riding a vehicle, collision with a moving vehicle while walking/running/standing/ sitting on the road, or fall from a moving vehicle. The burden of road traffic accidents (RTA) is a leading cause of all trauma admissions in hospitals worldwide. Road traffic injuries cause considerable economic losses to victims, their families, and to nations as a whole. These losses arise from the cost of treatment (including rehabilitation and incident investigation) as well as reduced/lost productivity (e.g. in wages) for those killed or disabled by their injuries and for family members who need to take time off work (or school) to care for the injured. Road traffic fatality in the Kingdom of Saudi Arabia (KSA) is the highest, accounts for 4.7% of all mortalities. Road injuries also are reported to be the most serious in this country, with an accident to injury ratio of 8:6. In this study, we try to focus on some causes of the accidents in KSA, so we can implement the prevention plan.

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.

scholarly journals Estimation of Safety Distance Between Vehicles on Highways Using YOLOv4 from Aerial Images

2021 ◽  
Author(s):  
Vitor Yeso Fidelis Freitas ◽  
Richardson Santiago Teles Menezes ◽  
Francisco Vidal ◽  
Helton Maia
Keyword(s):  
Network Architecture ◽  
Traffic Accidents ◽  
Aerial Images ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Average Precision ◽  
Moving Vehicle ◽  
Safety Distance ◽  
Moving Vehicles ◽  
Operational Errors

Traffic accidents are among the most worrying problems in modern life, often caused by human operational errors such as inattention, distraction, and misbehavior. Vehicle speed detection and safety distance measurement can help reduce these accidents. In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance and the vehicle’s speed on Brazilian highways. The investigation was conducted to model the YOLO algorithm for detecting vehicles in different network architecture configurations. The best results were obtained with the YOLO Full-608, reaching a mean Average Precision (mAP) of 97.44%. Additional computer vision approaches have been developed to calculate the speed of the moving vehicle and the safe distance between them. Therefore, the developed system allows that, based on detecting the safe distance between moving vehicles on the highways, accidents are predicted and possibly avoided.

scholarly journals Vehicle Detection and Traffic Prediction

2021 ◽  
Vol 9 (9) ◽  
pp. 1791-1794
Author(s):  
Ms. Aysha
Keyword(s):  
Linear Regression ◽  
Object Detection ◽  
Multiple Linear Regression ◽  
Police Officers ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Vehicle Speed ◽  
The Road ◽  
On The Road ◽  
Control Traffic

Abstract: On the road, vehicle detection processes are utilized for vehicle tracking, vehicle counting, vehicle speed, and traffic analysis. For vehicle detection, the Tensorflow object detection API method is employed. The Object Detection API in Tensorflow is a powerful tool that allows anyone to easily design and deploy effective picture recognition applications. Another way to control traffic is to use a traffic control system. Multiple linear regression is utilized to regulate the traffic system, while the OpenCV approach is used to identify vehicle speed. A system for fine payment is also offered. It makes police officers' jobs easier. The exact results of vehicle speed and traffic control are provided. Keywords: Vehicle detection, Tensorflow object detection API, Multiple linear regression, OpenCV, Fine payment

scholarly journals Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 324 ◽  
Author(s):  
Dae-Hyun Kim
Keyword(s):  
Detection Method ◽  
Impulse Radio ◽  
Lane Detection ◽  
Ultra Wideband ◽  
Vehicle Speed ◽  
Range Resolution ◽  
The Road ◽  
Detection Technology ◽  
On The Road ◽  
Wideband Radar

An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

scholarly journals Wheel Slip-based Road Surface Slipperiness Detection

2020 ◽  
Vol 14 (1) ◽  
pp. 186-193
Author(s):  
Jinhwan Jang
Keyword(s):  
Deep Learning ◽  
Traffic Accidents ◽  
Winter Season ◽  
High Rate ◽  
Support Vector ◽  
Wheel Slip ◽  
Moving Vehicle ◽  
The Road ◽  
The Difference ◽  
On The Road

Background: Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, also known as “black ice.” Methods: In this study, wheel slip, defined as the difference between both speeds of vehicular transition and wheel rotation, was used to detect road slipperiness. Three types of experiment cars were repeatedly driven on snowy and dry surfaces to obtain wheel slip data. Three approaches, including regression analysis, support vector machine (SVM), and deep learning, were explored to categorize into two states-slippery or non-slippery. Results: Results indicated that a deep learning model resulted in the best performance with accuracy of 0.972, only where sufficient data were obtained. SVM models universally showed good performance, with average accuracy of 0.965, regardless of sample size. Conclusion: The proposed models can be applied to any connected devices including digital tachographs and on-board units for cooperative ITS projects that gather wheel and transition speeds of a moving vehicle to enhance road safety in winter season though collecting followed by providing dangerous slippery spots on the road.

scholarly journals Forensic medical evaluation of dental-jaw injuries in cases of traffic accidents

2020 ◽  
Vol 26 (4) ◽  
pp. 5-10
Author(s):  
P.V. Plevinskis ◽  
V.D. Mishalov ◽  
S.V. Kozlov ◽  
N.M. Kozan ◽  
O.V. Dunayev
Keyword(s):  
Traffic Accidents ◽  
Morphological Features ◽  
The Body ◽  
Moving Vehicle ◽  
The Road ◽  
Vehicle Collision ◽  
Medical Evaluation ◽  
Health Disorders ◽  
On The Road ◽  
Dental Apparatus

Information about the differential diagnosis of human bodily injuries, which were formed when the body, wheel and bottom of a modern car came into contact with the body of a pedestrian; a person on the road surface, in the cabin of a modern car (driver and passengers), when a cyclist comes into contact with a car, in cases of combined types of car injury, is not enough. The purpose of the study is to increase the objectivity of forensic examinations by determining the criteria for assessing damage to the dental system in cases of the most common types of accidents: collision of moving vehicle with man; run over the body with a wheels or the bottom of vehicle; at an injury inside the vehicle on the basis of the analysis of morphological features and the mechanism of the specified damages. The archival materials of 130 forensic medical examinations of the municipal institution “Odessa Regional Bureau of Forensic Medical Examination” concerning victims of living persons and corpses as a result of traffic accidents that were accompanied by their injuries in the period 2015-2020 were used. The following research methods were used: anthropometric, morphometric, photographic, radiological, statistical. The article presents our own experience of improving the objectivity and provability of forensic examinations by determining the criteria for assessing damage to the dental system in cases of the most common types of vehicle: collision of moving vehicle with man; run over the body with a wheels or the bottom of vehicle; at an injury inside the vehicle on the basis of the analysis of morphological features and the mechanism of the specified damages. It is proved that according to the degree of gravity of physical injuries (health disorder or disability), damage to the dental apparatus in traffic accidents should be investigated only in cases of isolated injuries. In this case, fractures of the jaws, regardless of their nature, should be assessed as moderate injuries according to the criterion of long-term health disorders; Crown fractures, traumatic tooth dislocations, and soft tissue fatal wounds should be considered simple injuries that have caused short-term health disorders. Abrasions, bruises should be attributed to simple injuries. Thus, it is impractical to separately determine the severity of the injury of the dental system in cases run over the head with a wheels or the bottom of vehicle - in these cases, we always deal with gross, massive destruction of the bones of the skull.

scholarly journals Behavioral Dynamics of Pedestrians Crossing between Two Moving Vehicles

2020 ◽  
Vol 10 (3) ◽  
pp. 859 ◽  
Author(s):  
Soon Ho Kim ◽  
Jong Won Kim ◽  
Hyun-Chae Chung ◽  
Gyoo-Jae Choi ◽  
MooYoung Choi
Keyword(s):  
Traffic Accidents ◽  
Vehicle Speed ◽  
Mathematical Tool ◽  
Vehicle Type ◽  
Behavioral Dynamics ◽  
Moving Vehicles ◽  
Bearing Angle ◽  
Human Participants ◽  
Crossing Point ◽  
Insight Into

This study examines the human behavioral dynamics of pedestrians crossing a street with vehicular traffic. To this end, an experiment was constructed in which human participants cross a road between two moving vehicles in a virtual reality setting. A mathematical model is developed in which the position is given by a simple function. The model is used to extract information on each crossing by performing root-mean-square deviation (RMSD) minimization of the function from the data. By isolating the parameter adjusted to gap features, we find that the subjects primarily changed the timing of the acceleration to adjust to changing gap conditions, rather than walking speed or duration of acceleration. Moreover, this parameter was also adjusted to the vehicle speed and vehicle type, even when the gap size and timing were not changed. The model is found to provide a description of gap affordance via a simple inequality of the fitting parameters. In addition, the model turns out to predict a constant bearing angle with the crossing point, which is also observed in the data. We thus conclude that our model provides a mathematical tool useful for modeling crossing behaviors and probing existing models. It may also provide insight into the source of traffic accidents.

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.

Real-Time V2V Communication With a Machine Learning-Based System for Detecting Drowsiness of Drivers

2021 ◽  
Vol 13 (4) ◽  
pp. 35-50
Author(s):  
Ahmed Y. Awad ◽  
Seshadri Mohan
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Economic Losses ◽  
Drowsy Driving ◽  
The Road ◽  
V2v Communication ◽  
Physical Injuries ◽  
On The Road ◽  
The U.S

This article applies machine learning to detect whether a driver is drowsy and alert the driver. The drowsiness of a driver can lead to accidents resulting in severe physical injuries, including deaths, and significant economic losses. Driver fatigue resulting from sleep deprivation causes major accidents on today's roads. In 2010, nearly 24 million vehicles were involved in traffic accidents in the U.S., which resulted in more than 33,000 deaths and over 3.9 million injuries, according to the U.S. NHTSA. A significant percentage of traffic accidents can be attributed to drowsy driving. It is therefore imperative that an efficient technique is designed and implemented to detect drowsiness as soon as the driver feels drowsy and to alert and wake up the driver and thereby preventing accidents. The authors apply machine learning to detect eye closures along with yawning of a driver to optimize the system. This paper also implements DSRC to connect vehicles and create an ad hoc vehicular network on the road. When the system detects that a driver is drowsy, drivers of other nearby vehicles are alerted.

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