In-vehicle data recorders for monitoring and feedback on drivers’ behavior

Toledo, Tomer; Musicant, Oren; Lotan, Tsippy

doi:10.1016/j.trc.2008.01.001

Cited by 258 publications

(123 citation statements)

References 7 publications

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“…Based on the information collected in the database, real-time and off-line feedback can be delivered to drivers. In this role, IVDR effects to reduce the occurrence of risky behaviors have been recognized in various studies [24,17].…”

Section: In-vehicle Data Recorders (Ivdr)mentioning

confidence: 99%

“…Drivers are also categorized as green, yellow and red, according to the rate of their events: green drivers perform less than 20 events per 10 driving hours, yellow drivers perform between 20 and 50 events per 10 driving hours, and red drivers perform more than 50 events per 10 driving hours. For previous studies conducted with this system and further details, see Toledo et al [24] and Prato et al [23].…”

Section: In-vehicle Data Recorders (Ivdr)mentioning

confidence: 99%

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Are young drivers as careful as they deem? In vehicle data recorders and self reports evaluations

Albert

Lotan²,

Toledo

et al. 2014

Eur. Transp. Res. Rev.

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Purpose This paper aims to explore the driving behavior of young drivers few years after licensure based on two different evaluation approaches, which are used in a complementary manner. The evaluation was done with respect to driving exposure and trip safety. Methods The evaluation is based on two data collection approaches, which were compiled for the same trips: The first, In Vehicle Data Recorders (IVDR), which were installed in the young drivers' vehicles for a study period of 8 months. The second, Self-Reports (SR), which were provided by the young drivers at random times throughout the study period. These data have been compared and used in a complementary manner in order to provide an understanding of participants' driving behavior. Results The results show high correlation of driving exposure which was self-reported and these obtained from IVDR. The results also indicate that young drivers clearly perceived themselves as being safer drivers than they are, according to IVDR findings. In addition variables available only in the SR e.g., the presence of passenger in the car were found to affect the trip risk level. Conclusions The analysis obtained should be considered as exemplifying the potential of what may be accomplished and understood using these evaluation approaches.

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Section: In-vehicle Data Recorders (Ivdr)mentioning

confidence: 99%

Section: In-vehicle Data Recorders (Ivdr)mentioning

confidence: 99%

Are young drivers as careful as they deem? In vehicle data recorders and self reports evaluations

Albert

Lotan²,

Toledo

et al. 2014

Eur. Transp. Res. Rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In some of our previous studies, the usefulness of providing feedback from technology was explored, and it was found to be linked to a decrease in the events rate (Musicant and Lampel 2010;Toledo et al, 2008). The fourth-year study was therefore designed to test the effect of feedback as part of the follow-up.…”

Section: Methodsmentioning

confidence: 99%

“…Driving behavior and patterns throughout the year following licensure was explored in a previous study conducted between the years 2006-2008 with 120 novice young drivers in Israel. For a detailed description of this study and its results, see Lotan and Toledo (2007), Toledo et al (2008) and Prato et al (2010).…”

Section: Introductionmentioning

confidence: 99%

“…This advanced recording equipment was installed in the drivers' cars and, consequently, all trips made by the vehicle were monitored and information regarding trip characteristics and safety levels were stored and conveyed to participants. The application of IVDR to continuously monitor driving patterns and behavior, not only during a crash event, has been recognized in various studies (Toledo et al, 2008;Dingus et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

Albert

Musicant

Lotan

et al. 2011

Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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Summary: This paper aims to evaluate how young drivers drive a few years after licensure. Driving behavior in the fourth year of driving is compared to that of the first year, based on data from In-Vehicle Data Recorders (IVDR). Young drivers' cars were equipped with the same IVDR systems in both study periods. The comparison revealed that, in general, driving patterns did not change significantly. The difference in risky behaviour between weekdays and weekends was more prominent in the fourth year than in the first year. In addition, an interesting improvement occurred at the end of the fourth-year study period. The analysis results obtained should also be considered an example of the potential of what may be done with this kind of data.

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Novel hawk swarm‐optimized deep learning classification with K‐nearest neighbor based decision making for autonomous vehicle movement controller

Qingmiao,

Dinghua

2024

Concurrency and Computation

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SummaryNowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM‐GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line‐based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K‐nearest neighbor (KNN)‐based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal‐to‐noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively.

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In-vehicle data recorders for monitoring and feedback on drivers’ behavior

Cited by 258 publications

References 7 publications

Are young drivers as careful as they deem? In vehicle data recorders and self reports evaluations

Are young drivers as careful as they deem? In vehicle data recorders and self reports evaluations

Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

Novel hawk swarm‐optimized deep learning classification with K‐nearest neighbor based decision making for autonomous vehicle movement controller

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