Safe Driving Education System ASSIST -- Education Anywhere, Anytime

Goshi, Kazuaki; Hayashi, Masaki; Matsunaga, Katsuya

doi:10.1109/uic-atc.2012.154

Cited by 3 publications

(4 citation statements)

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“…A decrement in the number of risky maneuvers above 50% is observed in most cases, considering the variables, factors, road scenarios, and driver behavior. This demonstrates a positive influence of using the assistant and shows the importance of driver behavior in improving road safety [4,5]. In addition, statistical validation (confidence level of 95%) is carried out to verify that the results obtained for the driving study in a real environment, are equally valid for other drivers (population) [39].…”

Section: Discussionmentioning

confidence: 72%

“…Thus, these systems assist the driver in different tasks, such as getting oriented, increasing fuel-consumption efficiency, and proving useful information about both vehicle tracking and motion. The goal is to maintain the driver attention in the road and with that improve safety on the roads [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to alert the driver by suggesting actions while the driving process is being carried out; therefore, careless situations can be avoided and with these, traffic accidents. While there have been studies that have developed intelligent driving assistants in real vehicles, as in the case of Yay et al [5] and Kazuaki et al [4], there are not many studies combining the road accident risk in the assistance process. This is particularly useful because, for example, some studies have shown that at higher speeds, the probability of road accidents becomes greater [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Terán

Navarro

et al. 2020

Sensors

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Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Terán

Navarro

et al. 2020

Sensors

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“…In recent years, traffic accidents caused by improper driving of drivers or fatigue driving have made the field of safe driving [1] and automatic driving [2] a hot field of research. [7] with Hough transform to detect and mark lane lines.…”

Section: Introductionmentioning

confidence: 99%

Lane Recognition Algorithm Using the Hough Transform Based on Complicated Conditions

Zhang¹,

Ma²

2019

JCC

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At present, most lane line detection methods are aimed at simple road surface. There is still no good solution for the situation that the lane line contains arrow, text and other signs. The edge left by markers such as arrow and text will interfere with the detection of lane lines. In view of the situation of arrow mark and text mark interference between lane lines, the paper proposes a new processing algorithm. The algorithm consists of four parts, Gaussian blur, image graying processing, DLD-threshold (Dark-Light-Dark-threshold) algorithm, correlation filter edge extraction and Hough transform. Among them, the DLD-threshold algorithm and related filters are mainly used to remove the identification interference between lane lines. The test results on the Caltech Lanes dataset are given at the end of the article. The result of verification of this algorithm showed a max recognition rate of 97.2%.

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