Head motion coefficient-based algorithm for distracted driving detection

2019 ◽  
Vol 53 (2) ◽  
pp. 171-188 ◽  
Author(s):  
Kwok Tai Chui ◽  
Wadee Alhalabi ◽  
Ryan Wen Liu
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Video Stream ◽  
Detection Algorithm ◽  
Distracted Driving ◽  
Head Motion ◽  
Content Type ◽  
Cell Phone Use ◽  
The Road ◽  
Video Frames

PurposeConcentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.Design/methodology/approachThe system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.FindingsThe accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.Originality/valueThe system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

Video Stream Mining for On-Road Traffic Density Analytics

2012 ◽  
pp. 182-194
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

Video Stream Mining for On-Road Traffic Density Analytics

2013 ◽  
pp. 1019-1030
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

scholarly journals Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

2021 ◽  
Vol 18 (13) ◽  
pp. 7155
Author(s):  
Susana García-Herrero ◽  
Juan Diego Febres ◽  
Wafa Boulagouas ◽  
José Manuel Gutiérrez ◽  
Miguel Ángel Mariscal Saldaña
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Distracted Driving ◽  
Road Traffic Accidents ◽  
The Road ◽  
Young Drivers ◽  
Gender And Age ◽  
Serious Injuries ◽  
The Impact

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

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.

Mitigation of road traffic crash hazard in Pakistan

2014 ◽  
Vol 23 (5) ◽  
pp. 567-585
Author(s):  
Muhammad Masood Rafi ◽  
Ashar Hashmat Lodi ◽  
Muhammad Arsalan Effendi
Keyword(s):  
Road Traffic ◽  
Evaluation Model ◽  
Road Traffic Crash ◽  
Potential Hazard ◽  
Suggested Model ◽  
Male Population ◽  
Content Type ◽  
The Road ◽  
On The Road ◽  
The City

Purpose – Road traffic crashes (RTCs) result in creating significant social and economic hazard for affectees, their families and society. The purpose of this paper is to present studies which were conducted to study the patterns of RTCs in Karachi which is a metropolitan city of Pakistan. The studies were conducted on one of the busiest roads in the city named as Shara-e-Faisal. The influence and contribution of different factors in RTCs has been studied and hazardous road sections of Shara-e-Faisal have been identified. Based on the data analysis, an evaluation model has been suggested to reduce the hazard of RTCs on Shara-e-Faisal. The objective of the presented studies is to increase the present level of safety of road travel by reducing crashes on Shara-e-Faisal. Design/methodology/approach – Existing data of RTCs in Karachi have been analysed for the presented studies. For this purpose, Shara-e-Faisal was divided in sections of 1 km length to study the vehicle crash pattern. Location surveys were conducted to record physical conditions of this road. A cluster analysis was carried out to identify hazardous sections of the road. An evaluation model has been suggested in the end to reduce the hazard of RTCs by identifying hazardous road sections of Shara-e-Faisal. Findings – The analysis of the data revealed that the crashes were higher over weekend and on Monday. Male population, particularly young people, and motorcycle riders were the largest affectees of RTCs. In general, more daytime crashes were recorded as compared to nighttime crashes. The crashes in the mid block of the road and those involving rear-end collisions were higher. The hazardous road locations were related to poor road conditions. Statistical analysis indicated that alternate routes were required to reduce the RTC hazard on Shara-e-Faisal. Research limitations/implications – The paper is a small, but an original, contribution to identify a potential hazard which is faced by the community in the city. This is the first attempt (to the best of authors’ knowledge) to address the issue of RTCs in Karachi from an engineering view point. Practical implications – The suggested model can be employed by the authorities as a guideline to mitigate the hazard of road crashes in the country. Originality/value – The paper provides valuable information on the road traffic incidents, their pattern and contributing factors in one of the largest metropolis of Pakistan. The suggested model can become helpful in reducing RTCs in Pakistan.

Analysis of Driver's Characteristics Based on Internet of Vehicles

2014 ◽  
Vol 505-506 ◽  
pp. 1148-1152
Author(s):  
Jian Qun Wang ◽  
Xiao Qing Xue ◽  
Ning Cao
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Classification Model ◽  
Economic Losses ◽  
Lane Changing ◽  
Internet Of Vehicles ◽  
Factors Affecting ◽  
The Road ◽  
The University ◽  
Driver’S Characteristics

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

LIGHT REGULATION SYSTEMS IMPROVEMENT ANALYSIS TO FORECAST THE TRANSPORT FLOWS IN ALMATY

2021 ◽  
Vol 116 (1) ◽  
pp. 299-304
Author(s):  
Assel Aliyadynovna Sailau
Keyword(s):  
Environmental Pollution ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Light Regulation ◽  
Transport Network ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Transport Telematics

The number of vehicles on the roads of Almaty, Kazakhstan is growing from year to year. This brings about an increasing intensity and density of traffic flows in the streets which leads to congestion, decreasing speed of the traffic flow, increasing environmental pollution caused by car emissions, and which can potentially lead to the road traffic accidents (RTA), including fatalities. While the number of injuries grows up mainly due to drivers’ non-compliance with the speed limit, the environmental pollution is caused by longer traffic jams. Therefore, to reduce the level of road traffic injuries and emissions into the environment it is necessary to ensure the uniform movement of traffic flows in cities. Currently, one of the effective ways to do it is the use of transport telematics systems, in particular, control systems for road signs, road boards and traffic lights. The paper presents an analysis of existing systems and methods of traffic light regulation. The  analyses of the systems and methods are based on the use of homogeneous data, that is the data on standard parameters of traffic flows. The need in collecting and analyzing additional semi-structured data on the factors that have a significant impact on the traffic flows parameters in cities is shown as well. The work is dedicated to solving the problem of analysis and forecast of traffic flows in the city of Almaty, Kazakhstan. GPS data on the location of individual vehicles is used as the initial data for solving this problem. By projecting the obtained information onto the graph of the city's transport network, as well as using additional filtering, it is possible to obtain an estimate of individual parameters of traffic flows. These parameters are used for short-term forecast of the changes in the city's transport network.

SOME TRAFFIC SAFETY ISSUES AT UNREGULATED PEDESTRIAN CROSSINGS IN THE CITY

2021 ◽  
Vol 5 (12(81)) ◽  
pp. 26-32
Author(s):  
V. Volkov ◽  
E. Nabatnikova ◽  
E. Lebedev
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Hourly Interval ◽  
Safety Issues ◽  
The Road ◽  
Probability Estimates ◽  
Mode Of Transport ◽  
Speed Mode

The groups of participants of the pedestrian and automobile flows, whose actions cause the greatest danger to the occurrence of conflict situations in the zone of unregulated transition, are identified. The factors determining the likelihood of a traffic accident at an unregulated transition are systematized, for which probability estimates of the occurrence of road traffic accidents are calculated. As an estimated parameter, the hazard coefficient of a conflict point of an unregulated transition is proposed, which is determined by the ratio of the probability of a traffic accident in the real-time hourly interval to the average annual probability of a traffic accident reduced to the hourly interval. The dependences of the hazard ratio of an unregulated transition are established on the most significant factors: the speed mode of transport in the area before the transition and the state of the road surface.

scholarly journals An overview on pedestrians involved in traffic accidents in Latvia: Years 2010-2014

2018 ◽  
Vol 40 ◽  
pp. 01004 ◽  
Author(s):  
A. Bukova-Zideluna ◽  
A. Villerusa ◽  
A. Lama
Keyword(s):  
Injury Risk ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Capital City ◽  
Winter Period ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Related Factors ◽  
The Road ◽  
Adverse Weather

Latvian national road accident statistics shows that for the vulnerable road users’ situation is critical, since pedestrians are involved in more than a quarter of road traffic accidents. This paper gives an analysis on pedestrians involved in road traffic accidents based on the road safety accident database in Latvia for the years 2010–2014. The total number of cases does not change significantly, however there has been an increase in pedestrian fatality rates over the period. From the total number of traffic accidents with pedestrians involved 92.4% had injuries, 6.8% were lethal cases and others didn't suffer from injuries. Out of 342 fatalities 37.7% occurred during the winter period, 56.1% in adverse weather (overcast, fog, rain or snow), 69.9% during twilight or darkness and 26.9% on weekends. Out of all accidents 55.3% occurred in the capital city Riga, but fatality rate was higher on main state roads. 8.1% of the total number of pedestrians involved in road traffic accidents was found to have alcohol in their blood right after the road traffic accident. Fatality rate was higher for those with exceeded BAC. Pedestrian injury risk analysis was associated with demographical and traffic-related factors, urbanization, visibility and seasonal patterns.

Regression Analysis of Rural Road Traffic Accidents Causes

2011 ◽  
Vol 97-98 ◽  
pp. 1042-1045 ◽  
Author(s):  
Chuan Jiao Sun ◽  
Ru Yue Bai ◽  
Yuan Yuan Yu
Keyword(s):  
Regression Analysis ◽  
Traffic Safety ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Lower Grade ◽  
The Road ◽  
Rural Road ◽  
Safety Awareness

9238 traffic accidents data are collected in rural road of China. Through the data analysis, the main causes of rural road traffic accident are presented. The external environment, the participant features, road features and accident characteristics are involved. The regression analysis in SPSS is applied to find the relationship between the accident features. Overall, the rural road traffic accident was mainly due to in the rural area there are mass travel mode, lower grade roads, poorer safety awareness of traveler and the road is lack of traffic safety facilities and so on.

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