An introduction to splines for use in computer graphics & geometric modeling
An introduction to splines for use in computer graphics & geometric modeling
Vision and navigation for the Carnegie-Mellon navlab
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
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
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Simultaneous local and global state estimation for robotic navigation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Robust Lane Detection and Tracking in Challenging Scenarios
IEEE Transactions on Intelligent Transportation Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Probabilistic lane estimation for autonomous driving using basis curves
Autonomous Robots
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This paper describes an algorithm for estimating lane boundaries and curbs from a moving vehicle using noisy observations and a probabilistic model of curvature. The primary contribution of this paper is a curve model we call lateral uncertainty, which describes the uncertainty of a curve estimate along the lateral direction at various points on the curve, and does not attempt to capture uncertainty along the longitudinal direction of the curve. Additionally, our method incorporates expected road curvature information derived from an empirical study of a real road network. Our method is notable in that it accurately captures the geometry of arbitrarily complex lane boundary curves that are not well approximated by straight lines or low-order polynomial curves. Our method operates independently of the direction of travel of the vehicle, and incorporates sensor uncertainty associated with individual observations. We analyze the benefits and drawbacks of the approach, and show results of our algorithm applied to real world data sets.