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Developing World Model Data Specifications as Metrics for Sensory Processing for On-Road Driving Tasks

机译：开发用于公路驾驶任务的感官处理指标的世界模型数据规范

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The building of knowledge-intensive real-time intelligent control systems is one of the most difficult tasks humans attempt. It is motivated by the desire to create an artificial reasoning system that is capable of intelligent behavior. A critical question to be answered is how is the success of this effort is to be measured and evaluated. Measurement of the outward observable system behavior, while somewhat indicative does not really measure the correctness or quality of the system's capabilities. This is especially true in complex real-time control systems such as autonomous on-road driving. This paper describes an on-going effort at NIST to do task analysis and develop performance metrics for autonomous on-road driving. This project uses the NIST Real-time Control System (RCS, now referred to as 4D/RCS) design methodology and reference architecture to develop a task decomposition representational format for the on-road driving task knowledge. This task decomposition representation is used as the framework to further specify the world model entities, attributes, features, and events required for proper reasoning about each of the subtask activities. These world model specifications, in turn, are used as the requirements for the sensory processing system. These requirements identify those things that have to be measured in the environment, including their resolutions, accuracy tolerances, detection timing, and detection distances for each subtask activity. From these can be developed a set of performance metrics that allow validation of sensory processing by evaluating the world model representations it produces for each individual component subtask activity. In this way, taxonomies of autonomous capabilities can be developed and tested against these sensory processing and world model building performance metrics. Additional metrics can be developed to measure the performance characteristics of the behavior generation component with its planning and value judgment.

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