2007 Fiscal Year Final Research Report Summary
Aresearch on wide area traffic event detection systems by information fusion through ubiquitous sensor network
| Project/Area Number |
17300042
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KAMIJO Shunsuke The University of Tokyo, Institute of Industrial Science, Associate Professor (70334357)
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| Co-Investigator(Kenkyū-buntansha) |
SAKAUCHI Masao University of Tokyo, Institute of Industrial Science, Professor (30107370)
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| Project Period (FY) |
2005 – 2007
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| Keywords | ITS / Vision Sensor / Supersonic Wave Sensor / Pattern Recognition / Time Series Data Analysis / Driving Simulator |
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| Research Abstract |
In this project, we aimed at development of wide area traffic event detection systems for driver's assistance by employing network of different kinds of sensors such as vision sensors and supersonic wave sensors. We installed incident detection system to collect image data of accidents, and investigated such acquired accident images to reveal mechanisms arousing accidents. By the investigation, an interesting mechanism was revealed as such that shock waves caused by a small breaking behaviors at downstream traffic are amplified during propagating to upstream traffic, and the shock waves should be a major factor for the accidents on highways.
We thus developed sensor fusion network systems to detect propagation of such the shockwaves as soon as possible. The sensor fusion network systems employ image sensors and supersonic wave sensors for two kilometers long, and the collected data from the sensors will be summarized for the purpose of driver's assistance. The images sensors are able to
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acquire trajectory of each vehicle and vehicle count at each lane, while the supersonic wave sensors are able to acquire velocity of each vehicle and vehicle count at each lane. Velocities of vehicles can be calculated from the trajectories of vehicles of image sensors. By summarizing those velocities and vehicle counts from the sensors, we developed an algorithm to predict the state transition at each location caused by shock wave propagation. The algorithm defines boxels by dividing time-space cubes that represents velocity and vehicle count at each location at each time, and it modeled the state transition by learning six months data of the state transition. As a result, our algorithm was able to predict the state of each location at each time in 85-90% successful rate.
Finally, we examined the effect of driver's support system by employing forty monitor drivers consisting of men and women of wide ages. In general such the pattern recognition based system has uncertainty such as miss detections or false detections. By lowering threshold level for the detection, miss detections increase while false detection decrease. By elevating threshold level for the detection, miss detections decrease while false detection increase. Therefore, it is important to optimize parameters for pattern recognition in order to make the system most agreeable for the drivers. By the driving simulation, we determined the most appropriate parameters for the shock wave detection systems, and we validated the optimized system by applying to six months traffic data. Less
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