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.

scholarly journals A database development and analysis of selected side impact collisions in Toronto

2021 ◽  
Author(s):  
John Zaki Bou-Younes
Keyword(s):  
Traffic Accidents ◽  
Side Impact ◽  
Vehicle Speed ◽  
Software Release ◽  
Late Model ◽  
The City ◽  
Different Sources ◽  
Insight Into ◽  
Statistical Analysis Software ◽  
Selection Of

This thesis is based on the initial phase of a project that developed an in-depth collision database and performed an analysis of police reported side-impact collisions for the City of Toronto intersections between 1998 and 2000. Currently, collision data exists through several different sources in Ontario. The development of a database involving the amalgamation of collision forms, the selection of data fields, and the collection of real collision data from selected, thoroughly investigated side impact collisions involving late model vehicles (1998 and newer), is described. For analysis, Statistical Analysis Software Release 8.02 was used to investigate causation and causal factors of side impact collisions. Statistically significant collision factors determined by fault propensity included apparent driver action, driver age, front seat passenger age, maximum posted speed, approximate vehicle speed, road character, and number of lanes. For intersection collision propensity, statistically significant findings included the system used, presence of flashing signals, intersection legs, roadway volume, and intersection leg road classifications. It is anticipated that the findings from this analysis can provide insight into significant factors in side-impact collisions that will be applied with greater focus to the in-depth collision database, once developed. Traffic accidents

scholarly journals A database development and analysis of selected side impact collisions in Toronto

2021 ◽  
Author(s):  
John Zaki Bou-Younes
Keyword(s):  
Traffic Accidents ◽  
Side Impact ◽  
Vehicle Speed ◽  
Software Release ◽  
Late Model ◽  
The City ◽  
Different Sources ◽  
Insight Into ◽  
Statistical Analysis Software ◽  
Selection Of

This thesis is based on the initial phase of a project that developed an in-depth collision database and performed an analysis of police reported side-impact collisions for the City of Toronto intersections between 1998 and 2000. Currently, collision data exists through several different sources in Ontario. The development of a database involving the amalgamation of collision forms, the selection of data fields, and the collection of real collision data from selected, thoroughly investigated side impact collisions involving late model vehicles (1998 and newer), is described. For analysis, Statistical Analysis Software Release 8.02 was used to investigate causation and causal factors of side impact collisions. Statistically significant collision factors determined by fault propensity included apparent driver action, driver age, front seat passenger age, maximum posted speed, approximate vehicle speed, road character, and number of lanes. For intersection collision propensity, statistically significant findings included the system used, presence of flashing signals, intersection legs, roadway volume, and intersection leg road classifications. It is anticipated that the findings from this analysis can provide insight into significant factors in side-impact collisions that will be applied with greater focus to the in-depth collision database, once developed. Traffic accidents

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 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.

The Analysis and Research of Traffic Accident Velocity Based on C#

2014 ◽  
Vol 505-506 ◽  
pp. 1137-1142
Author(s):  
Li Lin ◽  
Ting Ting Lv
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Front Face ◽  
Vehicle Speed ◽  
Application Range ◽  
Collision Type ◽  
Side Collision ◽  
Speed Identification ◽  
Important Basis ◽  
Collision Angle

In the process of the traffic accidents confirmation, the identification of vehicle speed when accident occurred is often an important basis for accident confirmation. The paper firstly discusses the models of mechanics and solving method for the vehicle front face, rear end, sides face ,slanted side collision based on the theory of collision mechanics ,it describes how to identify the vehicle rate and collision angle based on the model simplification, the theoretical analysis for dealing with the complicated accidents. The common and formulas are studied based on the classical collision mechanics method. The application range, parameters involved in selection and influence of the formulas are analyzed in detail. Finally the program based on C# is developed according to the identified calculation process for vehicle speed of traffic accident. The vehicle speed is obtained by selecting the collision type, entering the relevant accident pattern, inputting the parameters and clicking the command button .The application can store, modify and display results conveniently , improve efficiency on vehicle speed identification effectively and reduce the processing cycle of traffic accident availably.

Research on Safety Vehicle Distance Recognition Based on Stereo Vision and DSP

2013 ◽  
Vol 333-335 ◽  
pp. 805-810 ◽  
Author(s):  
Rong Bao Chen ◽  
Ning Li ◽  
Hua Feng Xiao ◽  
Wei Hou
Keyword(s):  
Stereo Vision ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Intelligent Transportation ◽  
Research Area ◽  
Measurement Range ◽  
Vehicle Speed ◽  
Target Vehicle ◽  
Operation Rate ◽  
The Right

With the development of economy, there are an increasing number of cars as well as traffic accidents, thus intensifying the need to take measures to reduce traffic accidents and protect the safety of life and property. Vehicle distance is one of the most important indexes of traffic safety. The measurement of safety vehicle distance has become an increasingly hot research area of intelligent transportation. Through analyzing the basic principle of stereo vision and calibrating the parameters of the CCD sensors both inside and outside, this paper comes up with a method to measure the former vehicle distance based on stereo vision and DSP. Once the vehicle speed and distance form a non-security association, it will give a warning, and upload data or force speed-limiting. According to the different coordinates of the obtained images of the target vehicle from the left and the right sensor, this method can identify feature points, calculate distance to the target vehicle, and analyze the security of vehicle distance. The experimental results show that this method has wide measurement range, high measurement accuracy, and fast operation rate, thus it can meet the actual needs of the measurement of safe vehicle distance in intelligent transportation.

scholarly journals Shared and Independent Neural Representation Between Visual Perception and Mental Imagery

2021 ◽  
Author(s):  
Yingying Huang ◽  
Frank Pollick ◽  
Ming Liu ◽  
Delong Zhang
Keyword(s):  
Visual Perception ◽  
Mental Imagery ◽  
Activity Patterns ◽  
Neural Representation ◽  
Gabor Features ◽  
Visual Areas ◽  
Neural Substrate ◽  
Early Visual Cortex ◽  
Human Participants ◽  
Insight Into

Abstract Visual mental imagery and visual perception have been shown to share a hierarchical topological visual structure of neural representation. Meanwhile, many studies have reported a dissociation of neural substrate between mental imagery and perception in function and structure. However, we have limited knowledge about how the visual hierarchical cortex involved into internally generated mental imagery and perception with visual input. Here we used a dataset from previous fMRI research (Horikawa & Kamitani, 2017), which included a visual perception and an imagery experiment with human participants. We trained two types of voxel-wise encoding models, based on Gabor features and activity patterns of high visual areas, to predict activity in the early visual cortex (EVC, i.e., V1, V2, V3) during perception, and then evaluated the performance of these models during mental imagery. Our results showed that during perception and imagery, activities in the EVC could be independently predicted by the Gabor features and activity of high visual areas via encoding models, which suggested that perception and imagery might share neural representation in the EVC. We further found that there existed a Gabor-specific and a non-Gabor-specific neural response pattern to stimuli in the EVC, which were shared by perception and imagery. These findings provide insight into mechanisms of how visual perception and imagery shared representation in the EVC.

Moving Vehicle Detection Using Time Domain Statistical Features

2013 ◽  
Vol 471 ◽  
pp. 208-212 ◽  
Author(s):  
M.P. Paulraj ◽  
Hamid Adom Abdul ◽  
Marhainis Othman Siti ◽  
Sundararaj Sathishkumar
Keyword(s):  
Probabilistic Neural Network ◽  
Hearing Impaired ◽  
Statistical Features ◽  
Experimental Protocol ◽  
Moving Vehicle ◽  
Vehicle Type ◽  
Moving Vehicles ◽  
Machine Interface ◽  
Moving Vehicle Detection ◽  
Sound Signature

The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.

scholarly journals For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1829
Author(s):  
Minhee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Deep Neural Networks ◽  
Time Of Day ◽  
Automated Driving ◽  
Context Analysis ◽  
Driving System ◽  
Vehicle Type ◽  
Fast Development ◽  
Day Of The Week

Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.

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