| Project/Area Number |
22K04594
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 25010:Social systems engineering-related
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| Research Institution | Gunma University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
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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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2024: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2023: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | Traffic Accident / Autonomous Vehicle / Vehicle Automation / Traffic Accident Cost / Traffic Safety / Car Crash / Self-Driving Vehicle / Crash Cost |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
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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| Strategy for Future Research Activity |
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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