Preliminary study of the application of synthetic vision for obstacle avoidance on highways

Understanding and characterizing the forward environment of a ground vehicle is a pivotal element in determining the appropriate maneuver-response strategy while under varied degrees of vehicle automation. Potential degrees of automation span the probable near-term adoption of longitudinal crash countermeasure warning devices all the way through the longer-term objective of full vehicle automation. Between these extremes lies partially automated longitudinal crash avoidance, a potentially rich area of application for synthetic vision. This paper addresses the application of synthetic vision to vehicle automation from a systems perspective: from development of a collision avoidance framework, to application of the appropriate sensor-environmental descriptions with which synthetic vision applications can address. Obstacle detection modules, the human cognitive component and the dynamics of automated ground vehicle control comprise elements of this framework. Areas to fulfill a structured program and suggested areas for further in-depth research are identified.

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