Title: Multi-Sensor Lane Finding in Urban Road Networks
Authors: Albert Huang, David Moore, Matthew Antone, Edwin Olson, Seth Teller
Abstract: This paper describes a perception-based system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it fuses asynchronous, heterogenous sensor streams; it distributes naturally across several CPUs communicating only through message-passing; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the travel lane. We analyze the system's performance in the context of the 2007 DARPA Urban Challenge, where with five cameras and thirteen lidars it was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90~km urban course at speeds up to 40 km/h.
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