A Basic Study on Active Safety for Transportation Vehicle

最近，已经开发了许多附加系统来提高主动安全性、稳定性和操纵性。在作为驾驶员-车辆-环境系统的主动安全研究的早期阶段，了解人-机-环境系统的行为是很重要的。驾驶员的任务有三种，一种是对环境的识别，另一种是对要遵循的路线进行决策，最后一种是在输入车辆的情况下进行路线跟踪。这项研究为用神经网络系统分析包括这三个任务的驾驶员控制算法奠定了基础。结果表明，该模型的分析结果较好地反映了真实驾驶员的控制方式和在时间域上的响应。通过对神经网络驾驶员模型的灵敏度分析，分析了驾驶员的控制算法。分析发现，从主动安全的角度来看，道路形状与灵敏度之间的关系是非常重要的。

Recently, many additional systems to improve active safety, stability, and handling have been developed. At an ealy stage of the research for active safety as driver-vehicle-environment system, it is important to know the behavior of man-machine-environment systems. For such purpose, to know the control algorithm of drivers is required.There are three types of drivers task, one is recognition for the environment, the other one is decision making for the course to follow, and the last one is course tracking with a vehicle input. This research forms the first step in analyzing control algorithm for a driver including these three tasks with neural network system. It is found that the anaytical results with this model well express the control manners of the real driver with the response at time domain. Furthermore, the control algorithm of the driver is analyzed using the sensitivity analysis to the neural network driver model. From the analysis, it is found that the relationship between road shape and the sensitivities is very important at the view point of active safety.