Aresearch on wide area traffic event detection systems by information fusion through ubiquitous sensor network

泛在传感器网络信息融合广域交通事件检测系统研究

基本信息

批准号：
17300042
负责人：
KAMIJO Shunsuke
金额：
$ 10.32万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2007
项目状态：
已结题

项目摘要

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 … More 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

在这个项目中，我们的目标是通过采用视觉传感器和超声波传感器等不同类型的传感器网络来开发用于辅助驾驶员的广域交通事件检测系统。我们安装了事件检测系统来收集事故的图像数据，并进行了调查。获取事故图像以揭示引发事故的机制通过调查，揭示了一个有趣的机制，即下游交通的微小破坏行为引起的冲击波在传播到上游交通的过程中被放大，并且冲击波应该是造成事故的主要因素。发生的事故因此，我们开发了传感器融合网络系统，以尽快检测这种冲击波的传播。传感器融合网络系统采用了两公里长的图像传感器和超声波传感器，并将传感器收集的数据汇总起来。图像传感器能够获取每辆车的轨迹和每条车道的车辆数量，而超声波传感器能够获取每辆车的速度和每条车道的车辆数量。计算自通过总结来自传感器的速度和车辆，我们开发了一种算法来预测由冲击波传播引起的每个位置的状态转换，该算法通过划分代表速度和车辆的时空立方体来定义盒子。每个位置每次的车辆数量，并通过学习六个月的状态转换数据来建模状态转换。因此，我们的算法能够以 85-90% 的成功率预测每个位置每次的状态。最后，我们通过雇用四十名不同年龄的男性和女性监控驾驶员来检查驾驶员支持系统的效果。一般来说，这种基于模式识别的系统存在诸如漏检或误检的不确定性。通过提高检测阈值，误检测会减少，而误检测会增加。因此，优化模式识别参数以使系统最适合驾驶员非常重要。驾驶模拟，我们确定了冲击波检测系统最合适的参数，并通过应用六个月的交通数据来验证优化的系统。