Abstract: A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

Abstract: A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: receiving a lane marking, expressed in god's view, associated with a current view; fitting, in a post-processing module, the lane marking in an arc by using a set of parameters; generating a lane template, using the set of parameters, the lane template including features of the lane marking associated with the current view and features of the arc; and feeding the lane template associated with the current view for detection of a next view.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth; off-line training a lane detection algorithm by using the ground truth, the lane detection algorithm using parameters that express a lane marking in an arc; on-line generating a predicted lane marking; comparing the predicted lane marking against the ground truth; and off-line refining the lane detection algorithm.

Abstract: A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: receiving a lane marking, expressed in god's view, associated with a current view; fitting, in a post-processing module, the lane marking in an arc by using a set of parameters; generating a lane template, using the set of parameters, the lane template including features of the lane marking associated with the current view and features of the arc; and feeding the lane template associated with the current view for detection of a next view.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth associated with lane markings expressed in god's view; receiving features from at least one of a hit-map image and a fitted lane marking, wherein the hit-map image includes a classification of pixels that hit a lane marking, and the fitted lane marking includes pixels optimized based on the hit-map image; and training a confidence module based on the features and the ground truth, the confidence module configured to determine on-line whether a fitted lane marking is reasonable, using parameters that express a lane marking in an arc.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth; off-line training a lane detection algorithm by using the ground truth, the lane detection algorithm using parameters that express a lane marking in an arc; on-line generating a predicted lane marking; comparing the predicted lane marking against the ground truth; and off-line refining the lane detection algorithm.