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The Intelligent Systems Division of the National Institute of Standards and Technology has been engaged for several years in developing real-time systems for autonomous driving. A road detection program is an essential part of the project. Previously we developed an adaptive road detection system based on color histograms using a neural network. This, however, still required human involvement during the initialization step. As a continuation of the project, we have expanded the system so that it can adapt to the new environment without any human intervention. This system updates the neural network continuously based on the road image structure. In order to reduce the possibility of misclassifying road and non-road, we have implemented an adaptive road feature acquisition method.

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In this paper, we describe our recent efforts in grouping sensory data into meaningful entities. Our grouping philosophy is based on perceptual organization principles using gestalt hypotheses where we impose structural regularity on sensory primitives stemming from a common underlying cause. We present results using field data from UGVs and outline the utility of our research in object recognition and tracking for autonomous vehicle navigation. In addition, we show how the grouping efforts can be useful for constructing symbolic topological maps when data from different sensing modalities are fused in a bottom-up and topdown fashion.

Our previous work on road detection suggests the usage of prior knowledge in order to improve performance. In this paper we will explain our motivation for a novel approach, define requirements and point out issues, particularly concerning the representation of road depending on the use, which need to be addressed. The proposed system will provide symbolic data for high-level processes and guidance for low-level processes. Furthermore, we will outline the recognition approach based on previously discussed requirements and issues. This paper has a visionary character based on our experience with road detection for autonomous road vehicles.