Lane Estimation for Autonomous Vehicles

Autonomous ground vehicles, or self-driving cars, require a high level of situational awareness in order to operate safely and efficiently in real-world conditions. A system able to quickly and reliably estimate the location of the roadway and its lanes based upon local sensor data would be a valuable asset both to fully autonomous vehicles as well as driver assistance technologies. To be most useful, the system must accom-modate a variety of roadways, a range of weather and lighting conditions, and highly dynamic scenes with other vehicles and moving objects. Lane estimation can be modeled as a curve estimation problem, where sensor data provides partial and noisy observations of curves. The number of curves to estimate may be initially unknown and many of the observations may be outliers and false detections (e.g., due to tree shadows or lens flare). The challenge is to detect lanes when and where they exist, and to update the lane estimates as new observations are received