Abstract: One variation of a method for autonomous navigation includes, at an autonomous vehicle: recording a first image via a first sensor and a second image via a second sensor during a scan cycle; calculating a first field of view of the first sensor and a second field of view of the second sensor during the scan cycle based on surfaces represented in the first and second images; characterizing a spatial redundancy between the first sensor and the second sensor based on an overlap of the first and second fields of view; in response to the spatial redundancy remaining below a threshold redundancy, disabling execution of a first navigational action—action informed by presence of external objects within a first region of a scene around the autonomous vehicle spanning the overlap—by the autonomous vehicle; and autonomously executing navigational actions, excluding the first navigational action, following the scan cycle.

Abstract: One variation of a method for calculating nominal paths for lanes within a geographic region includes: serving a digital frame of a road segment to an annotation portal; at the annotation portal, receiving insertion of a lane marker label, for a lane marker represented in the digital frame, over the digital frame; calculating a nominal path over the road segment and defining a virtual simulator environment for the road segment based on the lane marker label; during a simulation, traversing the virtual road vehicle along the nominal path within the virtual simulator environment and scanning the virtual simulator environment for collisions between the virtual road vehicle and virtual objects within the virtual simulator environment; and, in response to absence of a collision between the virtual road vehicle and virtual objects in the virtual simulator environment, updating a navigation map for the road segment with the nominal path.

Abstract: One variation of a method for training and refining an artificial intelligence includes: training a neural network on a training set to identify objects in optical images; receiving a manual label attributed to an optical image—recorded by a road vehicle during its operation—by a human annotator; passing the optical image through the neural network to generate an automated label attributed to the optical image; in response to the manual label differing from the automated label, serving the optical image to a human annotator for manual confirmation of one of the manual label and the automated label; appending the training set with the optical image containing one of the manual label and the automated label based on confirmation received from the human annotator; and retraining the neural network, with the training set, to identify objects in optical images.

Abstract: One variation of a method for calculating nominal paths for lanes within a geographic region includes: serving a digital frame of a road segment to an annotation portal; at the annotation portal, receiving insertion of a lane marker label, for a lane marker represented in the digital frame, over the digital frame; calculating a nominal path over the road segment and defining a virtual simulator environment for the road segment based on the lane marker label; during a simulation, traversing the virtual road vehicle along the nominal path within the virtual simulator environment and scanning the virtual simulator environment for collisions between the virtual road vehicle and virtual objects within the virtual simulator environment; and, in response to absence of a collision between the virtual road vehicle and virtual objects in the virtual simulator environment, updating a navigation map for the road segment with the nominal path.

Abstract: One variation of a method for autonomous navigation includes, at an autonomous vehicle: recording a first image via a first sensor and a second image via a second sensor during a scan cycle; calculating a first field of view of the first sensor and a second field of view of the second sensor during the scan cycle based on surfaces represented in the first and second images; characterizing a spatial redundancy between the first sensor and the second sensor based on an overlap of the first and second fields of view; in response to the spatial redundancy remaining below a threshold redundancy, disabling execution of a first navigational action—action informed by presence of external objects within a first region of a scene around the autonomous vehicle spanning the overlap—by the autonomous vehicle; and autonomously executing navigational actions, excluding the first navigational action, following the scan cycle.

Abstract: One variation of a method for training and refining an artificial intelligence includes: training a neural network on a training set to identify objects in optical images; receiving a manual label attributed to an optical image—recorded by a road vehicle during its operation—by a human annotator; passing the optical image through the neural network to generate an automated label attributed to the optical image; in response to the manual label differing from the automated label, serving the optical image to a human annotator for manual confirmation of one of the manual label and the automated label; appending the training set with the optical image containing one of the manual label and the automated label based on confirmation received from the human annotator; and retraining the neural network, with the training set, to identify objects in optical images.