Naturalistic Driving Study for Older Drivers based on the DriveSafe App

Bergasa, Luis M.; Araluce, Javier; Romera, Eduardo; Barea, Rafael; Lopez-Guilen, Elena; Egido, Javier Del; Hernanz-Mayoral, Carlos A.

doi:10.1109/itsc.2019.8917079

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…In order to perform driving behavior analytics and identify differences between driver groups or states, [26] exploited smartphone data collected during a naturalistic driving experiment from a sample of 20 older drivers who participated for 1 to 2 weeks. To this end, a Gaussian model was developed based on the penalties obtained for the seven events (acceleration, braking, steering, weaving, drifting, overspeeding and car following) that were collected and taken into account.…”

Section: A Driver Profile Identification Studiesmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of "optimal" human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer.

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Section: A Driver Profile Identification Studiesmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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“…erefore, continuous recording can overcrowd the smartphone memory. For example, DriveSafe app [276] records videos only before and after some event.…”

Section: Rq1: Which Gamified Mobile Apps Related To Driving Behavior Improvement Are Currently Available In the Common Mobile Apps Reposimentioning

confidence: 99%

“…Profile data are manually entered by the driver when creating a new account. Both driving and profile data allow to detect driving style and subsequently classify the driver by offering him a score [277] and allow to access detailed driving reports such as hard acceleration events, hard braking, aggressive steering (turns), weaving (lane changes), overspeeding, short car following, driver scoring, and driver behaviors classification [276]. For example, the Amber Driver app [203] offers trip history allowing the driver to access detailed driver reports highlighting indicators such as distance covered, driving behavior, and idle time.…”

Section: Rq3: What Data Were Collected and How Ey Werementioning

confidence: 99%

“…Levi-Bliech et al [21] studied the effects of a fleet management application on driver behavior precisely before driving; the results show that the more drivers use the application before driving, the less likely they are to be involved in driving incidents and subsequently proving good driving. To allow appropriate driving assistance, Bergasa et al [276] provided a driver condition estimation system that collects information about the driver and provides condition estimation before driving.…”

Section: Driving Data (55%)mentioning

confidence: 99%

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Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

hafidy

Rachad

Idri

et al. 2021

Mobile Information Systems

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Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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“…Merging data from all these different sources provides a comprehensive picture of real-time events to understand the relationship between driver, vehicle, traffic environment during normal or conflict situations, and traffic crashes. NDS provide a unique perspective toward understanding how driver factors effect on-road behavior, offering critical insight into roadway and safe driving recommendations, overall and among high-risk driver groups (e.g., aging, those with diabetes) [10]- [15].…”

Section: Introductionmentioning

confidence: 99%

Deep Insight: A Cloud Based Big Data Analytics Platform For Naturalistic Driving Studies

Venkatachalapathy

Rahman

Raj

et al. 2023

IJAE

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Naturalistic driving studies (NDS) are an increasingly popular method to research driving behavior. They often result in large amounts of data varying in source and format (videos, spatial, and time-series data). Traditional data processing systems and analytical methods are not equipped to handle the large influx of data, often ranging from terabytes to petabytes. Previously, big data analytics platforms have been designed to address specific use cases of intelligent transport systems such as traffic flow prediction, transportation planning, and traffic safety. Similarly, there is a need for robust data systems for storing, mining, visualizing, and analyzing big naturalistic data. This paper presents a comprehensive cloud-based AI platform, Deep Insight, designed for data management, modeling, and enhanced annotations of naturalistic driving data. The platform capitalizes on Amazon Web Services, hosting a repository of public and privately collected NDS datasets with tool integration for data annotation and machine learning modeling that permits data analysis and inference. This end-to-end framework provides effective and reliable tools for storing, processing, annotating, and modeling NDS datasets. Additionally, the platform hosts a metric dashboard for benchmarking and displaying the performance of diverse analytical models using a standard dataset. The authors present a case study classifying a driver's head movement to demonstrate this framework's workflow using Deep Insight and integrated tools. This cloud-based platform offers a wide range of cost, access, scalability, and security benefits, supporting goals to create a one-stop, standardized destination for analyzing naturalistic driving data and studying driver behavior.

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Naturalistic Driving Study for Older Drivers based on the DriveSafe App

Cited by 6 publications

References 11 publications

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

Deep Insight: A Cloud Based Big Data Analytics Platform For Naturalistic Driving Studies

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