DriverSonar

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongbo Jiang ◽  
Jingyang Hu ◽  
Daibo Liu ◽  
Jie Xiong ◽  
Mingjie Cai
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Economic Loss ◽  
Steering Wheel ◽  
Acoustic Features ◽  
Acoustic Source ◽  
False Acceptance Rate ◽  
Driving Behaviors ◽  
The Arts ◽  
Dangerous Driving

Dangerous driving due to drowsiness and distraction is the main cause of traffic accidents, resulting in casualties and economic loss. There is an urgent need to address this problem by accurately detecting dangerous driving behaviors and generating real-time alerts. Inspired by the observation that dangerous driving actions induce unique acoustic features that respond to the signal of an acoustic source, we present the DriverSonar system in this paper. The proposed system detects dangerous driving actions and generates real-time alarms using off-the-shelf smartphones. Compared with the state-of-the-arts, the DriverSonar system does not require dedicated sensors but just uses the built-in speaker and microphone in a smartphone. Specifically, DriverSonar is able to recognize head/hand motions such as nodding, yawning, and abrupt adjustment of the steering wheel. We design, implement and evaluate DriverSonar with extensive experiments. We conduct both simulator-based and and real driving-based experiments (IRB-approved) with 30 volunteers for a period over 12 months. Experiment results show that the proposed system can detect drowsy and distraction related dangerous driving actions at an precision up to 93.2% and a low false acceptance rate of 3.6%.

scholarly journals Real-time Aggressive Driving Detection System based on In-vehicle Information using LoRa Communication

2020 ◽  
Vol 308 ◽  
pp. 06001
Author(s):  
Yongsu Jeon ◽  
Chanwoo Kim ◽  
Hyunwook Lee ◽  
Yunju Baek
Keyword(s):  
Real Time ◽  
Communication Networks ◽  
Traffic Accidents ◽  
Detection System ◽  
Aggressive Driving ◽  
Safe Driving ◽  
Driving Behaviors ◽  
Interior Data ◽  
Dangerous Driving ◽  
Vehicle Information

Safe driving has attracted a significant amount of attention in recent years owing to the increase in the complexity of the driving environment. There are many research studies focusing on detection of aggressive driving that may cause traffic accidents. In this paper, we propose a system for acquiring vehicles’ interior data and thereby detecting dangerous driving conditions. The system is designed to transmit the information acquired to a data server using Long Range (LoRa) communication networks. Through experimentation, we confirm that the proposed system can detect aggressive driving behaviors in real time and store them on the data server through LoRa communication. We evaluated techniques for acquiring in-vehicle information on 14 vehicles and confirmed that data can be extracted from most of the commonly available vehicles.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

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 Lived Experience of Transportation Network Company Driver About the Causes of High-risk Driving Behaviors: A Thematic Analysis

2021 ◽  
Vol 6 (3) ◽  
pp. 169-178
Author(s):  
Aref Shayganmehr ◽  
◽  
Fatemeh Hazratian ◽  
Robabeh Emrouzi ◽  
◽  
...  
Keyword(s):  
High Risk ◽  
Cause Of Death ◽  
Internal Control ◽  
Traffic Accidents ◽  
Human Life ◽  
World Health ◽  
External Control ◽  
Driving Behaviors ◽  
Study Results ◽  
Abnormal Behaviors

Background: Nowadays, industrialization, using cars and consequently traffic jams are part of human life which grows every day. Along with the expansion of communication and transportation speed, the number and severity of traffic accidents increases. According to the World Health Organization, traffic accidents are now recognized as the ninth cause of death worldwide. In Iran, traffic accidents after cardiovascular diseases are the second leading cause of death. Materials and Methods: This qualitative study was designed and implemented to determine driver’s views and opinions in two cities of Warsaw, Poland, and Tabriz, Iran, about driver’s high-risk behaviors. This study was conducted using in-depth interviews with 27 drivers. The study results were divided into four main questions about driving behaviors, reasons for driving abnormal behaviors, the prevalence and causes of abnormal behaviors, and suggested measures to correct these behaviors. Results: The study results were divided into six main themes of driving norms, individual factors, social factors, external factors, driving skills, and driving laws. Our findings indicate that drivers are more likely to rely on individual skills in driving in unacceptable conditions. In other words, they ignore the rules and regulations. But, when driving in high standards conditions and good facilities, drivers accept the rules and prioritize them. Conclusion: Internal control should be considered a helpful complement to external control, and that external control provides the highest efficiency when it comes with internal control. To internalize norms and observe driving laws and regulations, the authors suggest removing obstacles such as distrust among drivers regarding the effectiveness of driving laws, unawareness of breaking the laws, lack of job satisfaction, low level of participation, and structural barriers. Also, roads and vehicle safety must be improved along with a better track of the drivers’ behavior.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Charles Marks ◽  
Arash Jahangiri ◽  
Sahar Ghanipoor Machiani
Keyword(s):  
Real World ◽  
Traffic Accidents ◽  
Network Models ◽  
Multistage Process ◽  
Neural Network Models ◽  
Risky Driving ◽  
Vehicle Accidents ◽  
Driving Behaviors ◽  
Novel Approach ◽  
Risky Driving Behaviors

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

Research on Intelligent Steering Wheel Technology Based on Multi Sensor

2014 ◽  
Vol 488-489 ◽  
pp. 1130-1133
Author(s):  
Yuan Bai ◽  
Xiao Dong Tan
Keyword(s):  
Traffic Safety ◽  
Automobile Industry ◽  
Traffic Accidents ◽  
Drunk Driving ◽  
Steering Wheel ◽  
Private Car ◽  
Driving Condition ◽  
Fatigue Driving

At present, the automobile industry is developing rapidly, the private car is widely popularized, and the hidden dangers of traffic safety exist. The phenomenon of drunk driving and fatigue driving becomes more and more serious, and the improvement for steering wheel could effectively prevent traffic accidents. This paper introduces and analyzes the intelligence of steering wheel in three major aspects, they respectively include intelligent grip detection, which tests if a driver is of fatigue driving; hart rate detection, which tests if a driver is in normal driving condition; alcohol detection, which tests if a driver drinks too much, and it predicts the possibility of accident from the drivers state, and timely gives out signal to warn the driver.

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