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Robust Lane Detection and Tracking Prasanth Jeevan Esten Grotli Motivation Autonomous driving Driver assistance (collision avoidance, more precise driving directions) Some Terms Lane detection - draw boundaries of a lane in a single frame Lane tracking - uses temporal coherence to track boundaries in a frame sequence Vehicle Orientation- position and orientation of vehicle within the lane boundaries Goals of our lane tracker Recover lane boundary for straight or curved lanes in suburban environment Recover orientation and position of vehicle in detected lane boundaries Use temporal coherence for robustness Starting with lane detection Extended the work of Lopez et. al. 2005’s work on lane detection Ridgel feature Hyperbola lane model RANSAC for model fitting Realtime Our extension: Temporal coherence for lane tracking The Setup Data: University of Sydney (Berkeley-Sydney Driving Team) 640x480, grayscale, 24 fps Suburban area of Sydney Lane Model: Hyperbola 2 lane boundaries 4 parameters 2 for vehicle position and orientation 2 for lane width and curvature Features: Ridgels Picks out the center line of lane markers More robust than simple gradient vectors and edges Fitting: RANSAC Robustly fit lane model to ridgel features Setup Setup Setup The Setup Data: University of Sydney 640x480, grayscale, 24 fps Suburban area of Sydney Lane Model: Hyperbola 2 lane boundaries 4 parameters 2 for vehicle position and orientation 2 for lane width and curvature Features: Ridgels Picks out the center line of lane markers More robust than simple gradient vectors and edges Fitting: RANSAC Robustly fit lane model to ridgel features Lane Model Assumes flat road, constant curvature L and K are the lane width and road curvature ? and x0 are the vehicle’s orientation and position ? is the pitch of the camera, assumed to be fixed Lane Model v is the image row of a lane boundary uL and uR are the image column of the left and right lane boundary, respectively The Setup Data: University of