Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Rapidly Adapting Machine Vision for Automated Vehicle Steering
IEEE Expert: Intelligent Systems and Their Applications
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It is well recognized that knowledge on the lane and road geometry is a prerequisite for reliable function of the majority of automotive safety applications, especially those that deal with vehicles' lateral control. An effort to exploit all the available road and lane related information is presented in this work. It includes redundant information - from secondary sensor detections, e.g. stationary objects detected by the laser scanner - and data from dedicated sources of information, e.g. on-board digital map, and lane detection vision systems. The test vehicle system comprises a CMOS camera that identifies lane markings, while a laser scanner detects road borders position. Road geometry information from digital map databases is also fused with the above providing a complete representation of the road environment. In this paper the four individual estimators (data from digital map, camera, vehicle dynamics and laser scanner) and the combined fuser (with three different approaches) are presented and compared. A thorough examination of the system's performance using real data recordings took place and the results are reported, as well.