Two wheeler rash drive detection using smartphones

Sasidhar, Kalyan; Upasini, Aswini

doi:10.1109/comsnets.2019.8711455

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…e authors in [42] proposed an innovative driver behavior monitoring system in real-time, because of vehicle dynamics, road design values, and passenger comfort. To identify and detect the abnormal driving behavior, e.g., lane changing, weaving, and sudden braking by using pattern matching techniques were carried out in [43]. e study did not consider various road types, vehicles and placement positions of the phone, and variable sampling rates, and their effect on the pattern.…”

Section: Reckless Driving Detection Systemsmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

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Section: Reckless Driving Detection Systemsmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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“…The algorithms have been proved experimentally [14]. And in 2019, Sasidhar and Upasini developed a method uses a smartphone accelerometer for detecting and identifying abnormal driving behaviour such as lane changing, weaving, and sudden braking [15]. Recently, Rishu Chhabra and his colleagues designed and implemented an accelerometer to detect any unexpected changes in acceleration, sharp turns, and braking using gyroscope.…”

Section: Introductionmentioning

confidence: 99%

A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors

Hamad

Atiya²,

Al-Libawy

2022

IJ-AI

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<p>Dynamic vehicle weight measuring, weigh-in-motion (WIM), is an important metric that can reflect significantly vehicle driving behaviour and in turn, it will affect both safety and traffic status. Several accurate WIM systems are developed and implemented successfully. These systems are using under road weighing sensor which are costly to implement. Moreover, it is costly and not very practical to embed a continuous weighing system in used cars. In this work, a low-cost varied-speed weigh-in-motion approach was suggested to continuously measuring vehicle load based on the response of smartphone sensors which is a reflection of vehicle dynamics. This approach can apply to any moving vehicle at any driving speed without the need for extra added hardware which makes it very applicable because smartphone is widely used device. The approach was tested through a six-trips experiment. Three capacities of load had been designed in this approach to be classified using a neural network classifier. The classification performance metrics are calculated and show an accuracy of 91.2%. This accuracy level is within error limits of existing WIM systems especially for high speed and proved the success of the suggested approach.</p>

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Vehicle Management System To Reduce The Risky Factors Using Embedded Technology Enhanced With Wi-Fi

Jacintha

Murugan²,

Kaviya³

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Medium to medium is a quickly developing technique, which renders enhanced client servicing, occupation betterments via best usage of assets, enhanced techniques and production via communicating between distant machinery and vehicle Management System. Here, the vehicle status and location can be determined. Vehicle Tracker utilize GPS devices to give an account of the location information to the server. Accident is unplanned harm, is an unwanted, accidental, and unintentional consequence that must have been precluded had considerations and if there is no proper system human lives will be in danger. The main purpose of this technology is to identify an accident and give an indication signal to the control room, so that the affected individual can seek some support. The severity of the incident can be identify from the control room, without going to the sot in person. If this model is fix all vehicle, it will be very useful to identify how many vehicles undergoes in that specified accident and the extent to which it has taken place. In order that the aid from control room will be able to rescue and protect driver or passengers. The present board designed the tracking of vehicle and systems for accidents alert that makes it extremely valuable and utile. This board indicates to us driver abnormal condition and as well as accident identification. It additionally identifies the driver drowsiness.

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Two wheeler rash drive detection using smartphones

Cited by 5 publications

References 15 publications

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors

Vehicle Management System To Reduce The Risky Factors Using Embedded Technology Enhanced With Wi-Fi

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