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.

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.

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.

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 Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

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.

Determination of Resistance to Abrasive Wear. VIII. Influence of Softeners in Tests with the Akron-Croydon and Du Pont Machines

1949 ◽  
Vol 22 (1) ◽  
pp. 259-262
Author(s):  
J. F. Morley
Keyword(s):  
Unit Area ◽  
Published Data ◽  
The Road ◽  
Abrasive Surface ◽  
Abrasive Power ◽  
The Difference ◽  
On The Road ◽  
The Right ◽  
Abrasion Testing

Abstract These experiments indicate that softeners can influence abrasion resistance, as measured by laboratory machines, in some manner other than by altering the stress-strain properties of the rubber. One possible explanation is that the softener acts as a lubricant to the abrasive surface. Since this surface, in laboratory abrasion-testing machines, is relatively small, and comes repeatedly into contact with the rubber under test, it seems possible that it may become coated with a thin layer of softener that reduces its abrasive power. It would be interesting in this connection to try an abrasive machine in which a long continuous strip of abrasive material was used, no part of it being used more than once, so as to eliminate or minimize this lubricating effect. The fact that the effect of the softener is more pronounced on the du Pont than on the Akron-Croydon machine lends support to the lubrication hypothesis, because on the former machine the rate of wear per unit area of abrasive is much greater. Thus in the present tests the volume of rubber abraded per hr. per sq. cm. of abrasive surface ranges from 0.03 to 0.11 cc. on the du Pont machine and from 0.0035 to 0.0045 cc. on the Akron-Croydon machine. On the other hand, if the softener acts as a lubricant, it would be expected to reduce considerably the friction between the abrasive and the rubber and hence the energy used in dragging the rubber over the abrasive surface. The energy figures given in the right-hand columns of Tables 1 and 3, however, show that there is relatively little variation between the different rubbers. As a test of the lubrication hypothesis, it would be of interest to vary the conditions of test so that approximately the same amount of rubber per unit area of abrasive is abraded in a given time on both machines; this should show whether the phenomena observed under the present test conditions are due solely to the difference in rate of wear or to an inherent difference in the type of wear on the two machines. This could most conveniently be done by considerably reducing the load on the du Pont machine. In the original work on this machine the load was standardized at 8 pounds, but no figures are quoted to show how abrasion loss varies with the load. As an addition to the present investigation, it is proposed to examine the effect of this variation with special reference to rubbers containing various amounts and types of softener. Published data on the influence of softeners on the road wear of tire rubbers do not indicate anything like such large effects as are shown by the du Pont machine. This throws some doubt on the value of this machine for testing tire tread rubbers, a conclusion which is confirmed by information obtained from other workers.

Anti-Lock Wheel Slip Control With an Adaptive Searching Algorithm for the Optimal Slip

2015 ◽  
Author(s):  
Ning Pan ◽  
Liangyao Yu ◽  
Lei Zhang ◽  
Zhizhong Wang ◽  
Jian Song
Keyword(s):  
Lyapunov Theory ◽  
Modulation Amplitude ◽  
Wheel Slip ◽  
Slip Control ◽  
The Road ◽  
Road Friction ◽  
Speed Fluctuation ◽  
On The Road ◽  
Pressure Modulation ◽  
Abs Algorithm

An adaptive searching algorithm for the optimal slip during ABS wheel slip control is proposed. By taking advantage of the fluctuation of wheel slip control, the direction towards the optimal slip can be found, and the target slip calculated by the algorithm asymptotically converged to the optimal slip, which is proved using the Lyapunov theory. A gain-scheduling wheel slip controller is developed to control the wheel slip to the target slip. Simulations on the uniform road and on the road with changed friction are carried out to verify the effectiveness of the proposed algorithm. Simulation results show that the ABS algorithm using the proposed searching algorithm can make full use of the road friction and adapts to road friction changes. Comparing with the conventional rule-based ABS, the pressure modulation amplitude and wheel speed fluctuation is significantly reduced, improving control performance of ABS.

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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