Distracted Driver Monitoring with Smartphones: A Preliminary Literature Review

Kaiser, Christian; Stocker, Alexander; Papatheocharous, Efi

doi:10.23919/fruct52173.2021.9435545

Cited by 3 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A systematic literature review [57][58][59] is the method chosen to study the topic, and it contains a set of steps: planning, scoping, searching, selecting, reporting, and analysing. A preliminary execution of the method has already been published [23], whereas as an extension to the method, a snowballing search complements the results reported and analysed in this work (cf. Figure 1).…”

Section: Methodsmentioning

confidence: 84%

“…The method of snowballing was used to complement and enrich the research results, presented in the preliminary systematic literature review [23], with additional papers. These papers could not be found with the systematic literature review process executed since they did not come up in the search step.…”

Section: Snowballingmentioning

confidence: 99%

“…These papers could not be found with the systematic literature review process executed since they did not come up in the search step. Snowballing serves as a valuable complement to the authors' previous work [23] by effectively incorporating recent research findings that were challenging to identify through the systematic literature approach without the requirement of carrying out some steps in repetition. This method holds the potential to yield more pertinent results, with particular emphasis on the inclusion of updated or newer findings, especially with the forward snowballing search.…”

Section: Snowballingmentioning

confidence: 99%

“…The start set we have used is considered a good set because it contains an adequate number of papers relevant to the topic originating from different communities (cf. [23], Table 3), as well as a good mix of journal and conference publications (44% and 56%, respectively). Furthermore, in the Start set, different authors are represented from a mix of countries (i.e., Canada, China, Egypt, Germany, Greece, India, Philippines, Singapore, and the USA).…”

Section: Snowballingmentioning

confidence: 99%

“…The aim of this paper is to extend a previously published preliminary literature review of scientific peer-reviewed published work on driver distraction using smartphones [23]. Even though there have been several other literature reviews published on the topic of driver distraction, in general (e.g., [2,5,6,24]), and driver distraction monitoring (e.g., [3]), none of them have exclusively focused on distracted driver monitoring with smartphone-based systems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Papatheocharous,

Kaiser,

Moser

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

show abstract

Section: Methodsmentioning

confidence: 84%

Section: Snowballingmentioning

confidence: 99%

Section: Snowballingmentioning

confidence: 99%

Section: Snowballingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Papatheocharous,

Kaiser,

Moser

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Driver Distraction Detection Using Artificial Intelligence and Smart Devices

Papatheocharous,

Buffoni,

Maurer

et al. 2024

Intelligent Secure Trustable Things

View full text Add to dashboard Cite

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, has so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by smart devices, usually used while driving, such as smartphones and smartwatches. They are repeatedly a source of distraction for drivers through calls, messages, notifications and apps usage. However, such devices can also be used for driver behaviour monitoring (like driver distraction detection), as current developments show. As vehicle manufacturer-independent devices, which are usually equipped with adequate sensor technology, they can provide significant advantages and opportunities. This work illustrates the opportunities in using smartphones and wearables to detect driver distraction. The overall architecture description of the concept, called Smart Devices Distracted Driving Detection, is presented together with a series of initial experiments of a proof-of-concept. Artificial Intelligence and more especially Machine Learning is used to assess driving distractions using smart devices in a comprehensive manner.

show abstract

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

González-Saavedra

Figueroa

Céspedes

et al. 2022

Sensors

View full text Add to dashboard Cite

The design of cooperative advanced driver assistance systems (C-ADAS) involves a holistic and systemic vision that considers the bidirectional interaction among three main elements: the driver, the vehicle, and the surrounding environment. The evolution of these systems reflects this need. In this work, we present a survey of C-ADAS and describe a conceptual architecture that includes the driver, vehicle, and environment and their bidirectional interactions. We address the remote operation of this C-ADAS based on the Internet of vehicles (IoV) paradigm, as well as the involved enabling technologies. We describe the state of the art and the research challenges present in the development of C-ADAS. Finally, to quantify the performance of C-ADAS, we describe the principal evaluation mechanisms and performance metrics employed in these systems.

show abstract

Distracted Driver Monitoring with Smartphones: A Preliminary Literature Review

Cited by 3 publications

References 45 publications

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Driver Distraction Detection Using Artificial Intelligence and Smart Devices

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

Contact Info

Product

Resources

About