Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3360
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dc.contributor.authorVithanage, W-
dc.contributor.authorMadushan, H-
dc.contributor.authorMadushanka, T-
dc.contributor.authorLokuliyana, T-
dc.contributor.authorWijekoon, J-
dc.contributor.authorChandrasiri, S-
dc.date.accessioned2023-03-10T04:13:51Z-
dc.date.available2023-03-10T04:13:51Z-
dc.date.issued2022-11-30-
dc.identifier.issn2472-7598-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3360-
dc.description.abstractReducing ever-increasing road accidents is a big concern worldwide. Sri Lanka had the highest rate of road fatalities in the past few years, rapidly increasing daily. Among many factors, traffic signs, potholes, and vehicle mechanical malfunctions significantly impact road safety. Most accidents result from a lack of awareness, ignorance, and negligence of drivers. While many high-end vehicles are equipped with technologies such as intelligent road sign recognition systems and air suspension systems, most cars in the market only come with basic driving instruments. Therefore, there is a need for a universal driver assistance system that can be plugged into any vehicle to assist drivers in minimising road casualties. To this end, this study discusses Neural Networks, Machine Learning and IoT technologies to develop an intelligent system that is capable of detecting and analysing road signs, road potholes, vehicles’ internal system malfunctions, and road accidents and notifying drivers in real-time and inform authorities such as hospitals and police stations to be aware of accidents to minimise further casualties. This portable device is based on a Raspberry Pi microprocessor. It uses a web camera, an onboard diagnostic tool (OBD) and an accelerometer to process traffic sign footages, vehicle sensor data and movement data of the vehicle. Yielded results showed that the proposed system was 90% accurate.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer);-
dc.subjectSmart Driver Assistanceen_US
dc.subjectTraffic Signen_US
dc.subjectPotholeen_US
dc.subjectVehicle Malfunctionen_US
dc.subjectAccident Detectionen_US
dc.titleSmart Driver Assistance for Traffic Sign, Pothole, Vehicle Malfunction, and Accident Detectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICTer58063.2022.10024095en_US
Appears in Collections:Department of Computer Systems Engineering
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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