In recent years, automated vehicle researches move on to the next stage, that is auto-driving experiments on public roads. Major challenge is how to robustly drive at complicated situations such as narrow or non-featured road. In order to realize practical performance, some static information should be kept on memory such as road topology, building shape, white line, curb, traffic light and so on. Currently, some measurement companies have already begun to prepare map database for automated vehicles. They are able to provide highly-precise 3-D map for robust automated driving. This study focuses on what kind of data should be observed during automated driving with such precise database. In particular, we focus on the accurate localization based on the use of lidar data and precise 3-D map, and propose a feature quantity for scan data based on distribution of clusters. Localization experiment shows that our method can measure surrounding uncertainty and guarantee accurate localization.

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Capabilities and complexity of manufacturing systems are increasing and striving for an integrated manufacturing environment. Availability of alternative process plans is a key factor for integration of design, process planning and scheduling. This paper describes an algorithm for generation of alternative process plans by extending the existing framework of the process plan networks. A class diagram is introduced for generating process plans and process plan networks from the viewpoint of the integrated process planning and scheduling systems. An incomplete search algorithm is developed for generating and searching the process plan networks. The benefit of this algorithm is that the whole process plan network does not have to be generated before the search algorithm starts. This algorithm is applicable to large and enormous process plan networks and also to search wide areas of the network based on the user requirement. The algorithm can generate alternative process plans and to select a suitable one based on the objective functions.

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