| 研究課題/領域番号 |
22K04594
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分25010:社会システム工学関連
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| 研究機関 | 群馬大学 |
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研究代表者 |
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| 研究分担者 |
荒木 幹也 群馬大学, 大学院理工学府, 教授 (70344926)
天谷 賢児 群馬大学, 大学院理工学府, 教授 (20221731)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
3,120千円 (直接経費: 2,400千円、間接経費: 720千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2022年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
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| キーワード | Traffic Accident / Autonomous Vehicle / Vehicle Automation / Traffic Accident Cost / Traffic Safety / Car Crash / Self-Driving Vehicle / Crash Cost |
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| 研究開始時の研究の概要 |
Autonomous Vehicles can reduce traffic accident frequency and severity. In this research, we will develop a modeling framework to calculate the cost of traffic accidents for Autonomous Vehicles, including the calculation of traffic accident risk and probability of injury.
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| 研究実績の概要 |
Microscopic traffic simulations to determine the baseline for traffic accident occurrence before deployment of autonomous vehicles continued during FY 2023. The research focused on rear-end traffic accidents between two vehicles, which represent a significant share of traffic accidents in Japan. Since autonomous vehicle diffusion can prevent traffic accidents caused by human error, this research focused on traffic accidents caused by distracted driving. A new method was developed to simulate driver distraction in a multi-agent traffic simulation model by varying the distraction duration and frequency parametrically. The method was used to conduct an accelerated test, where distraction time and frequency were set to unrealistically high values to obtain a large number of accidents in a short period of time.
Focusing on rear-end traffic accidents between two vehicles caused by distracted driving, the impact of autonomous vehicles use on traffic accident occurrence was evaluated. Traffic accidents were simulated using a single lane, straight road for a street located in the urban area of Kiryu City in Gunma Prefecture. The share of autonomous vehicles in the total number of vehicles was varied from 0 to 100% using increments of 10%. It was possible to demonstrate that traffic accident occurrence decreases linearly with the increase of the share of autonomous vehicles. Simulation results were used to estimate the number of traffic accidents that result in neck injury.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Estimation of the baseline for traffic accident occurrence before autonomous vehicle deployment starts has proven to be more difficult than expected. Results for the accelerated test for rear-end traffic accidents caused by distracted driving cannot be compared directly against historical data for traffic accidents due to the unrealistically high values for distraction duration and frequency used to obtain large amount of data for traffic accidents in a short period of time. In that sense, accelerated test results were normalized by the total traveled distance and the distraction frequency and compared against historical data for Japan, obtained dividing total traveled distance by road vehicles in Japan by the total number of traffic accidents. Since data for distracted driving duration and frequency for Japan were not available, data for research in the United States was used. Normalized results for the accelerated test are higher than values from historical data. Currently, the reasons for the overestimation in the accelerated test are being examined.
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| 今後の研究の推進方策 |
During FY 2024, microscopic traffic simulations of rear-end accidents caused by distracted driving will continue with the aim of identifying the causes for the overestimation of the normalized traffic accident occurrence; and improve the estimation of the baseline for traffic accident occurrence before autonomous vehicle deployment.
During FY 2023 an initial estimation of the impact of autonomous vehicle use on the occurrence of traffic accidents that cause neck injury was obtained. During FY2024, a more detailed assessment of the acceleration that vehicles experience during traffic accidents will be performed, with the aim of getting a more detailed assessment of the impact of autonomous vehicle use on traffic accident severity.
Results for traffic accident severity will be used to construct a cost model of the traffic accidents that allow to estimate the potential of autonomous vehicle to reduce traffic accident cost.
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