Modeling IoT Enabled Automotive System for Accident Detection and Classification
09 Mar 2020-pp 1-6
TL;DR: An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident, which uses Naïve Bayes classifier for classification.
Abstract: Millions of people get injured, disabled or die in automotive accidents each year. Knowledge about the type of road accident is invaluable to the emergency medical services providers for optimal planning and execution of the rescue operation. An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident. The system uses in-built sensors of passenger smartphone to detect and classify the accident as head-on collision, rollover or fall-off. The accuracy of the proposed system, which uses Naive Bayes classifier for classification, has been evaluated using precision, recall, F1 score and ROC curve.
Citations
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04 Jan 2022
TL;DR: Using mobile sensing, this study aims to collect and monitor the road surface conditions in the city of Dehradun, Uttarakhand, India and the collected data has been analyzed using the Artificial Neural Network technique, demonstrating the algorithm's effectiveness in detectingRoad surface conditions.
Abstract: Poorly constructed roads, as well as roads that are damaged with cracks or potholes, are some of the leading causes of road accidents, particularly in developing nations like India. Accurate assessment of road conditions and prompt transmission of such situations may allow interested agencies to take corrective action, reducing the number of road accidents and fatalities. With the rapid advancements in sensor technologies, data aggregation, and fusion algorithms, it is now possible to identify such road conditions and report them for prompt action by the concerned authorities. Using mobile sensing, this study aims to collect and monitor the road surface conditions in the city of Dehradun, Uttarakhand, India. In order to identify different road surfaces such as smooth roads, speed hump, and rumble strips, the collected data has been analyzed using the Artificial Neural Network technique. The proposed model has an accuracy of 97%, demonstrating the algorithm's effectiveness in detecting road surface conditions.
5 citations
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TL;DR: In this article, the authors proposed a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions.
Abstract: With smartphones being so ubiquitous, more connected and largely fitted with several types of sensors such as GPS, microphones, cameras, magnetometers, accelerometers, etc; there is an increasing opportunity in the development of smartphone-based sensor systems. In this article, we propose a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions. We also list the main advantages and limitations of the use of smartphone sensors systems in such fields.
4 citations
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TL;DR: This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident.
Abstract: Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naive Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score.
4 citations
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TL;DR: In this article , an Android smartphone-based end-to-end Internet of Things (IoT) system that can transmit accident information to emergency services and affected families once a vehicle accident is detected.
Abstract: Road accidents caused by several factors are responsible for the deaths and injuries of people. The majority of deaths occur within first few hours of an accident. If in case of an accident the victim becomes incapacitated, and there is no onlooker to help, then an accurate automated accident reporting system can significantly reduce the number of such deaths. This paper reports an Android smartphone-based end-to-end Internet of Things (IoT) system that can transmit accident information to emergency services and affected families once a vehicle accident is detected. Along with the detection, the classification of vehicle accidents can be very helpful in identifying suitable life-saving medical-aid and appropriate rescue operation equipment. The main objective of this research work is to develop a machine learning (ML) model that can detect as well as classify vehicle accidents accurately into eight categories. To enhance the classification efficacy of the system, a multi-sensor fusion framework has been proposed that incorporates various sensor-fusion techniques at different levels of its implementation along with several preprocessing methods such as 10-ms moving maximum, complementary filters, and a 2-sec sliding window. The framework uses Logistic Regression (LR) based stacked generalization approach to combine the decisions of three ML classifiers, which are based on Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) methods. The LR-based stacking classifier is found to be very accurate with an F1-score of 0.95, whose performance is significantly better than the performance of all individual base-classifiers. • A smartphone-based vehicle accident detection and classification (ADC) system is proposed. • The system can send emergency alerts to the nearest emergency services using IoT. • The system can classify eight types of accidents using stacking ensemble method. • The system implements a sensor-fusion framework to enhance classification accuracy. • Proposed system is highly accurate, cost-effective, retrofittable, and easy-to-install.
1 citations
Journal Article•
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TL;DR: In this paper, the ESP32 board collects GPS data from the GPS module and sends a notification containing all relevant information to the Blynk app, which helps to identify collisions quickly and send the information to emergency responders in a quick manner.
Abstract: An accident is an unanticipated and unintended occurrence. In view of increase in the number of fatalities in our country and the delay in receiving immediate response is the leading cause of death in road accidents, accounting for half of all deaths. This system helps to identify collisions quickly and send the information to emergency responders in a quick manner. When the flex and mems accelerometer sensor threshold value exceed the configured maximum limit due to an accident, the ESP32 board collects GPS data from the GPS module and sends a notification containing all relevant information to the Blynk app. This system provides tracking and transmits the specific location of the accident, when detected to the registered email through Blynk app.
References
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TL;DR: This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Network (PNN), to detect the occurrence of accidents on the Eisenhower expressway in Chicago, and shows that although SVM achieves overall higher accuracy, PNN outperforms SVM regarding the Detection Rate.
Abstract: Detecting accidents is of great importance since they often impose significant delay and inconvenience to road users. This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Network (PNN), to detect the occurrence of accidents on the Eisenhower expressway in Chicago. Accordingly, since the detection of accidents should be as rapid as possible, seven models are trained and tested for each machine learning technique, using traffic condition data from 1 to 7 min after the actual occurrence. The main sources of data used in this study consist of weather condition, accident, and loop detector data. Furthermore, to overcome the problem of imbalanced data (i.e., underrepresentation of accidents in the dataset), the Synthetic Minority Oversampling TEchnique (SMOTE) is used. The results show that although SVM achieves overall higher accuracy, PNN outperforms SVM regarding the Detection Rate (DR) (i.e., percentage of correct accident detections). In addition, while both models perform best at 5 min after the occurrence of accidents, models trained at 3 or 4 min after the occurrence of an accident detect accidents more rapidly while performing reasonably well. Lastly, a sensitivity analysis of PNN for Time-To-Detection (TTD) reveals that the speed difference between upstream and downstream of accidents location is particularly significant to detect the occurrence of accidents.
57 citations
"Modeling IoT Enabled Automotive Sys..." refers methods in this paper
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TL;DR: This work has devised a new approach to orientation estimation using inertial sensors only, based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing.
Abstract: Precise and reliable estimation of orientation plays crucial role for any mobile robot operating in unknown environment. The most common solution to determination of the three orientation angles: pitch, roll, and yaw, relies on the Attitude and Heading Reference System (AHRS) that exploits inertial data fusion (accelerations and angular rates) with magnetic measurements. However, in real world applications strong vibration and disturbances in magnetic field usually cause this approach to provide poor results. Therefore, we have devised a new approach to orientation estimation using inertial sensors only. It is based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing. In the final, the algorithm well outperformed the commercial AHRS solution based on magnetometer aiding.
47 citations
"Modeling IoT Enabled Automotive Sys..." refers methods in this paper
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TL;DR: The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles and shows promising results in terms of accuracy.
Abstract: Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.
45 citations
"Modeling IoT Enabled Automotive Sys..." refers methods in this paper
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TL;DR: H Dy Copilot, an application for automatic accident detection integrated with multimodal alert dissemination, via both eCall and IEEE 802.11p is presented, which successfully detects collisions, rollovers, performs the eCall along with sending Minimum Set of Data (MSD) and Decentralized Environmental Notification Message (DENM).
Abstract: The rapid technological growth is now providing global opportunities to enable intelligent transportation system (ITS) to tackle road accidents which is considered one of the world's largest public injury prevention problem. For this purpose, eCall is an initiative by European Union (EU) with the purpose to bring rapid assistance to an accident location. This paper1 presents HDy Copilot, an application for automatic accident detection integrated with multimodal alert dissemination, via both eCall and IEEE 802.11p (ITS-G5). The proposed accident detection algorithm receives inputs from the vehicle, via ODB-II, and from the smartphone sensors, namely the accelerometer, the magnetometer and the gyroscope. An Android smartphone is used as human machine interface, so that the driver can configure the application, receive road hazard warnings issued by other vehicles in the vicinity and cancel countdown procedures upon false road vehicle crash detection. The HDy Copilot is developed for Android OS as it provides open source APIs that allow access to its hardware resources. The application is implemented, tested and connected to an IEEE 802.11p based prototype. The generated results show that the application successfully detects collisions, rollovers, performs the eCall along with sending Minimum Set of Data (MSD) and Decentralized Environmental Notification Message (DENM).
43 citations
"Modeling IoT Enabled Automotive Sys..." refers methods in this paper
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TL;DR: This paper presents the development of a system that uses smartphones to automatically detect and report car accidents in a timely manner and adds a notification system to reduce the response time and perhaps help in reducing fatalities.
Abstract: Traffic accidents are a fact of life. While accidents are sometimes unavoidable, studies show that the long response time required for emergency responders to arrive is a primary reason behind increased fatalities in serious accidents. One way to reduce this response time is to reduce the amount of time it takes to report an accident. Smartphones are ubiquitous and with network connectivity are perfect devices to immediately inform relevant authorities about the occurrence of an accident. This paper presents the development of a system that uses smartphones to automatically detect and report car accidents in a timely manner. Data is continuously collected from the smartphone’s accelerometer and analyzed using Dynamic Time Warping (DTW) and the Hidden Markov Models (HMMs) to determine the severity of the accident, reduce false positives and to notify first responders of the accident location and owner’s medical information. In addition, accidents can be viewed on the smartphone over the Internet offering instant and reliable access to the information concerning the accident. By implementing this application and adding a notification system, the response time required to notify emergency responders of traffic accidents can potentially reduce the response time which may help in reducing fatalities.
36 citations
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