Special Considerations for TDOA Application to the Reconstruction of Traffic Accidents

2011 ◽  
Vol 255-260 ◽  
pp. 4090-4094
Author(s):  
Lei Zhang ◽  
Wen Hu Qin ◽  
Tie Jun He ◽  
Wei Nong Li
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Lower Cost ◽  
Accident Reconstruction ◽  
Final State ◽  
Time Difference Of Arrival ◽  
Time Position ◽  
Novel Method ◽  
Scene Investigation ◽  
Accident Scene

Investigation at scene is the key issue to Scene Traffic Accident Reconstruction(TAR). Although the photogrammetry technique is widely applied in China due to its convenience and lower cost, it can only provide the final state of the traffic accidents. In this paper, a novel method based on the time difference of arrival (TDOA) technique was proposed to monitor the location of the vehicles passing the accident-prone section on the highway. The methodology of application of TDOA to the Traffic accident scene investigation was proposed, and the arrangement of sensors on the accident-prone sections of the selected freeway is discussed. Based on the coordinates collected by TDOA receivers, real-time position of the each vehicle moving on the accident-prone section could be extracted respectively and when the accidents happen, the traces of the vehicles involved would be obtained. It could be concluded that this method is important supplement to photogrammetry technique during accident scene investigation and could make the forensic judgment of accident responsibility more accurate.

A New Positioning System for Traffic Accident Scene Investigation Integrated with RFID and WSN

2014 ◽  
Vol 602-605 ◽  
pp. 2495-2499
Author(s):  
Hong Song ◽  
Jun Tang ◽  
Jian Xiong Ming ◽  
Wei Jian Duan ◽  
Zhi Yong Yin
Keyword(s):  
Traffic Accident ◽  
Reference Points ◽  
End Points ◽  
Closed Curves ◽  
New Radio ◽  
Efficiency And Effectiveness ◽  
Isolated Points ◽  
Scene Investigation ◽  
Location Map ◽  
Accident Scene

In traffic accident scene investigation, the traditional measuring and positioning methods cannot meet current requirement for efficiency and effectiveness. Which means the Investigators need use tape to measure the distance between accident factors and reference points, and then mark the corresponding elements -- dots, straight lines, curves, closed curves and so on - on the diagram for traffic accident scene investigation. A new radio location system integrated with RFID and WSN has been established to solve the problem. The function of RFID is to clearly mark the ID, attribute and affiliation; WSN to acquire locations of factors from the sensors; ZIGBEE to transmit the combined data (RFID and WSN data) to computers through networks. Using special software to automatically processing the received data for a visualized location map, including the following processes of A) locating survey marks with the geometric algorithm. B) Defining the types of survey marks, such as isolated points, line end points, curve end points, intermediate points etc. C) Classifying the survey marks, such as front wheels, rear wheels, skid marks, body locations, position of fallout etc.

scholarly journals The Quality and the Complete Evidence Securing during the Traffic Crime Scene Investigation and its Relevance for Evidence Completion during the Traffic Accidents

2018 ◽  
Vol 20 (4) ◽  
pp. 76-81
Author(s):  
Peter Havaj
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Crime Scene ◽  
Direct Impact ◽  
Legal Evidence ◽  
Whole Process ◽  
Video Record ◽  
Complex Process ◽  
Scene Investigation ◽  
Virtual Element

Main purpose of this paper is to point out the problems considering the modern scientific usage of methods, ways and approaches, including the crime investigation of traffic accidents-collisions. We want to show the basic need of experienced traffic crime detective, his/her deep knowledge of the whole issue - the process of the traffic accident perpetrating as a complex process with the direct impact on the traffic crime detective work, which could be used in the process of the clearance the case, the video-record output created by program PC CRASH as the virtual element of legal evidence, enabling deeper knowledge of the whole process.

scholarly journals ACQUISITION AND ANALYSIS OF TRAFFIC ACCIDENT SCENE INFORMATION BASED ON LASER POINT CLOUD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 423-430
Author(s):  
Y. M. Zheng ◽  
Y. R. He ◽  
X. R. Wang ◽  
Q. J. Chen
Keyword(s):  
Point Cloud ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Rapid Development ◽  
Area Measurement ◽  
Point Cloud Data ◽  
Accident Investigation ◽  
Cloud Data ◽  
Accident Site ◽  
Accident Scene

Abstract. With the rapid development of the economy, the number of vehicles in China has increased rapidly, which has also brought about frequent ills in traffic accidents. How to improve the efficiency of on-site treatment of traffic accidents, and quickly and accurately conduct accident investigation and analysis is imminent. This paper was based on the point cloud to draw the accident scene DLG, and then used the local elevation difference method to automatically extract the point cloud data of the accident vehicle, and analyzes the vehicle speed calculation, the damage area measurement and the road surface flatness, as well as constructs the overall 3D scene of the accident scene. By analyzing the DLG of accident scene, the point cloud data and the constructed 3D model, which could quickly improve the efficiency of traffic accident investigation. The application results show that the method of information collection and rapid exploration of the accident site what based on the laser point cloud not only provides a basis for traffic accident treatment, but also effectively shortens the exploration time of accident site. At the same time, it cloud relieve the traffic congestion in a certain extent with the obvious results.

scholarly journals Improvement of the method for determining the distance between vehicles by photographic images during road traffic accident reconstruction

2021 ◽  
Vol 18 (1) ◽  
pp. 140-148
Author(s):  
S. A. Evtyukov ◽  
◽  
I. V. Vorozheikin ◽  
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Fuzzy Neural Network ◽  
Road Traffic ◽  
Accident Reconstruction ◽  
Road Traffic Accidents ◽  
Photographic Images ◽  
Fuzzy Neural ◽  
Materials Used

Some photographic materials used in the investigation of road traffic accidents are presented. The authors discuss the ways of improving the method of determining the distance between vehicles by photographic images in the reconstruction of a road traffic accident based on a fuzzy neural network.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

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.

Traffic accident and emission reduction through intermittent release measures for heavy fog weather

2015 ◽  
Vol 29 (25) ◽  
pp. 1550148 ◽  
Author(s):  
Jing Shi ◽  
Jin-Hua Tan
Keyword(s):  
Traffic Safety ◽  
Emission Reduction ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Emission Characteristics ◽  
Driving Behaviors ◽  
Ca Model ◽  
Reduce Emissions

Heavy fog weather can increase traffic accidents and lead to freeway closures which result in delays. This paper aims at exploring traffic accident and emission characteristics in heavy fog, as well as freeway intermittent release measures for heavy fog weather. A driving simulator experiment is conducted for obtaining driving behaviors in heavy fog. By proposing a multi-cell cellular automaton (CA) model based on the experimental data, the role of intermittent release measures on the reduction of traffic accidents and CO emissions is studied. The results show that, affected by heavy fog, when cellular occupancy [Formula: see text], the probability of traffic accidents is much higher; and CO emissions increase significantly when [Formula: see text]. After an intermittent release measure is applied, the probability of traffic accidents and level of CO emissions become reasonable. Obviously, the measure can enhance traffic safety and reduce emissions.

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

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