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Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

Shilpa Gite (Computer Science Department, Symbiosis Institute of Technology, Symbiosis Centre for Applied AI(SCAAI), Symbiosis International (Deemed University), Pune, India)
Ketan Kotecha (Computer Science Department, Symbiosis Institute of Technology, Symbiosis Centre for Applied AI(SCAAI), Symbiosis International (Deemed University), Pune, India)
Gheorghita Ghinea (Department of Computer Science, Brunel University, London, UK)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 16 August 2021

Issue publication date: 22 May 2023

279

Abstract

Purpose

This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.

Design/methodology/approach

Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.

Findings

There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.

Research limitations/implications

The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.

Social implications

As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.

Originality/value

This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

Keywords

Citation

Gite, S., Kotecha, K. and Ghinea, G. (2023), "Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment", International Journal of Pervasive Computing and Communications, Vol. 19 No. 3, pp. 325-342. https://doi.org/10.1108/IJPCC-11-2020-0192

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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