Intelligent vehicle (IV) is one of the core components of Intelligent Transportation System. From the view of pattern-covering & embedding, the robust improvement mechanism of noise-add is described, and then the controller design method based on noise-add is presented. Fuzzy logic is applied to partition and cover vehicle motion pattern space. The controller's ability to cover and embed vehicle motion patterns is improved by adding noise in the design. Thus the robustness of the control system is enhanced. Simulation results show the designed controller can adapt to non-linear structure with time-variant parameters and measure noise.

Based on analyzing the pure geometry and possibility statistics map matching algorithms, a hybrid map matching algorithm based on fuzzy comprehensive judgment is proposed by synthesizing the merits of two methods and the topology relation of the real road network, and using the concept of the fuzzy theory. The simulation results show the new algorithm can provide more reasonable matched result for vehicle positioning information.

A kind of second2order algorithm---recursive approximate Newton algorithm was given by Karayian2 nis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi2layer feed2forward neural networks, the second2order back2propagation recursive algorithm based generalized costcriteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second2order convergent rate. The performance and application prospect are analyzed. Lots of simulation ex2periments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multi2ple algorithm. The algorithm and selection of networks parameters are significant and the performance is more ex2cellent than BP algorithm and the second2order learning algorithm that was given by Karayiannis.

There exist some flaws in current intelligent vehicle lateral controller design methods. Aim at the flaws, a new kind of robust design method was proposed. In the new method, longitudinal velocity, cornering stiffness of the tires, time constant of steering actor and path's effects on vehicle lateral dynamic pattern were fully considered. Fuzzy logic was used as control strategy. Key parameters, that can exert dominant on vehicle lateral dynamics, were reasonably selected to produce design pattern, which is more complete and has more dynamics information than real pattern. GA was used to automatically search and optimize fuzzy logical controller under the design pattern. The simulations show that the new method is effective.

A practical route planning algorithm for vehicle navigation system is proposed in this paper. the shortest path algorithm with the restricted searching area. The algorithm takes advantage of the adjacent list data structure and the mechanism of restricted searching area, that is, the algorithm uses the spatial distribution feature of the real road network to restrict the searching area in reason. Finally, combining with its practical application in Real-Time Vehicle Navigation System (RTVNS), one example is given, the analysis in theory and the experimental results show that compared with the Dijkstra algorithm, the new algorithm can greatly approve the searching speed, and satisfy the needs of RTVNS.

依据大数定律及其相关的假设，用计算几何的若干知识，设计车辆定位导航系统的一种新定位算法Z该算法通过计算凸壳、凸多边形三角剖分、凸多边形面积及直径等诸量获得车辆运行的近似路线Z计算结果表明，用该算法可以提高车辆定位导航系统的定位精度，并优于基于卡尔曼滤波的GPS NU MM组合导航算法的结果Z

提出一种时间复杂度为O（n）的限制搜索区域距离最短路径规划算法（n为路网节点数）Z算法设计的基础是，经典Dijkstra 算法搜索时的无方向性及实际城市道路网络特有的空间分布特性Z算法实现采用邻接表数据结构和限制搜索区域的搜索机制，即利用实际城市道路网络的空间分布特性，合理限制算法的搜索区域Z结合路径规划算法在实时车辆导航系统中的实际应用，给出了该算法的应用实例，实验结果表明，该算法能将路网中任意两点间的最短路径解算时间控制在3s以内Z

基于模拟退火算法和非完全Beta函数，提出了一种自适应红外图像对比度增强方法。首先基于输入红外图像的灰度直方图给出一种有效的判据。对输入红外图像先利用所提出的判据判断图像的对比度类型，然后利用这个判据来确定灰度变换参数的搜索空间，进一步指导模拟退火算法的搜索方向和初值的选取。利用模拟退火算法在上述确定的灰度变换参数空间中搜索最佳的灰度变换参数，从而获得一条最佳的灰度变换曲线，实现对图像进行全局增强处理。实验结果表明，该算法在有效地提高红外图像整体对比度的同时，很好地保留了图像中的细节部分信息。算法在视觉质量上优于传统的直方图均衡法、反锐化掩膜法。

控制器设计可看作一个结构和参数搜索与优化的过程。本文以模糊逻辑（FL）为控制策略，以T-S 模糊神经网络（FNN）为控制结构，以遗传算法（GA）为参数搜索和优化工具，给出一种智能车辆横向运动控制器自动设计算法，并通过计算机仿真实现。结果表明，所设计的控制器具有较好的控制性能，能够有效地解决智能车辆横向运动中的非线性和参数时变性问题。

提出一种对图像矩阵中的列进行搜索，并用列边界灰度值方法进行设计的提取红外图像边缘的方法；分析了文中算法的时间复杂度1实验结果表明，文中算法能够快速有效地提取红外图像的边缘，并能满足红外自动目标识别的实际需要1