University of Tsukuba Reports Findings in Risk Management (A context-aware driver model for determining recommended speed in blind intersection situations): Risk Management

2021 NOV 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Risk Management is the subject of a report. According to news reporting out of Ibaraki, Japan, by NewsRx editors, research stated, “The near-miss events involving vulnerable road users can lead to serious accidents. Safe and careful expert drivers perform a hazard-anticipatory driving and they will naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category they have already developed, that is, predicting what can happen.”

Financial supporters for this research include Japan Science and Technology Agency, Japan Society for the Promotion of Science.

Our news journalists obtained a quote from the research from the University of Tsukuba, “In this study, our target situation consists of a cyclist attempting a road crossing at a blind spot. This study aims at developing a context-aware driver model for determining the recommended driving speed at blind intersections based on the analysis of near-miss-incidence database, which includes the data on driver behavior and road environmental factors just before the near-miss. First, we extracted the drive-recorder data using the management tool provided in the database. Second, risk, which is defined as the time margin for drivers to perform evasive actions to avoid a crash, was quantified for the extracted data using the safety-cushion time. The safety-cushion time can be observed as a result of the driver’s adjustment to the vehicle velocity depending on the given road environment. One of the key aspects in developing the context-aware driver model is to categorize the extracted near-miss data into two levels based on the risk quantifications: low- and high-risk events. The low- and high-risk events were regarded as a result of the driver’s appropriate adjustment of, and inability or failure to adjust the vehicle velocity depending on the given road environment, respectively. Third, based on a multiple linear regression analysis with low-risk event dataset, we constructed a context-aware driver model to produce the recommended vehicle speed depending on the given road environment. The road environment variables, determined by stepwise regression, were identified as factors that reduced or increased the vehicle velocity at blind intersections, and were incorporated into the model as predictors. Furthermore, we quantitatively visualized drivers setting the baseline for speed adjustment and increasing or decreasing the speed according to the given road environment context. Fourth, the model validation demonstrated a coefficient of determination ® of 0.20, and a mean absolute error (MAE) of 6.54 km/h on average in the 5-fold cross-validation. Finally, to investigate the effectiveness of the constructed driver model on safety performance, we used the dataset of high-risk events as test data. Theoretically, the constructed driver model guided the drivers to drive the vehicle at the recommended speed, and thus convert more than half of the high-risk events into low-risk events.”

According to the news editors, the research concluded: “These results indicate that the context-aware driver model is feasible to be used to adjust the approaching speed at blind intersections in accordance with the road environment factors.”

This research has been peer-reviewed.

For more information on this research see: A context-aware driver model for determining recommended speed in blind intersection situations. Accident Analysis & Prevention, 2021;163:106447. Accident Analysis & Prevention can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

Our news journalists report that additional information may be obtained by contacting Yuichi Saito, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8573, Ibaraki, Japan. Additional authors for this research include Fumio Sugaya, Shintaro Inoue, Pongsathorn Raksincharoensak and Hideo Inoue.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106447. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal Accident Analysis & Prevention is: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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