Microcontroller and Sensor Based Smart Biking System for Driver’s Safety

Sheshadri, Swathi; Raj, Shubham; Devaraj, D.

doi:10.1109/incos45849.2019.8951409

Cited by 13 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ensuring the secure operation of shared mobility systems mainly depends on monitoring the behavior of road users, especially bike and scooter riders, and minimizing the vulnerabilities they are exposed to on the road [17]. Specifically, most studies on ensuring bike/scooter rider safety focused on the use of smart helmets [18][19][20][21][22] or smart bikes [23][24][25] along with mobile applications [19,[23][24][25] or cloud-based databases [22,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Research has not only focused on mobility sharing effects on urban transportation but also on distribution techniques, route optimization, and precise demand forecasting [11][12][13][14][15][16]. Safety concerns sparked creative solutions, such as smart helmets, smart bikes, and mobile applications intended to protect road users, as these platforms have grown in popularity [17][18][19][20][21][22][23][24]. In addition, machine-learning techniques have been used to identify accidents and categorize traffic irregularities using sensor-collected data [26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility

Elwy,

Aburukba,

Al-Ali

et al. 2023

Future Internet

View full text Add to dashboard Cite

Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider’s safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility

Elwy,

Aburukba,

Al-Ali

et al. 2023

Future Internet

View full text Add to dashboard Cite

show abstract

“…The system based on RFID and FSR is built so not to enable the rider to start a two-wheeler without wearing a helmet, with the vehicle ignition being controlled by a cumulative decision by the RFID reader in the car and FSR in the helmet [15,16]. A technique is proposed for developing a safety system that combines a smart helmet and an intelligent bike to minimize the chances of two-wheeler accidents, bike theft, and drunk driving cases [17]. A smart ignition system is implemented to detect the alcohol level of the two-wheelers rider based on GSM, Bluetooth, and Node MCU [18].…”

Section: Review Of Literaturementioning

confidence: 99%

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities

Gehlot

Singh

Kuchhal

et al. 2021

Sensors

View full text Add to dashboard Cite

Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.

show abstract