Abstract: The present technology provides systems, methods, and devices that can dynamically augment aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle to keep navigating a route. As the autonomous vehicle navigates a route, the autonomous vehicle can determine that current data captured by at least one sensor of the autonomous vehicle describing a location is inconsistent with the primary map of the location. The autonomous vehicle can determine that a portion of the current data describes a second feature that is distinct from a first feature described by a primary map. The second feature can be added to an augmented map that is based on the primary map, and the position of the autonomous vehicle can be located with respect to the first feature rather than the second feature on the augmented map.

Abstract: The present technology provides systems, methods, and devices that can dynamically augment aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle to keep navigating a route. As the autonomous vehicle navigates a route, the autonomous vehicle can determine that current data captured by at least one sensor of the autonomous vehicle describing a location is inconsistent with the primary map of the location. The autonomous vehicle can determine that a portion of the current data describes a second feature that is distinct from a first feature described by a primary map. The second feature can be added to an augmented map that is based on the primary map, and the position of the autonomous vehicle can be located with respect to the first feature rather than the second feature on the augmented map.

Abstract: The present technology provides a system that can update aspects of an authoritative map portion stored on an autonomous vehicle using low-resolution data from the at least one sensor of the autonomous vehicle, and therefore avoids the need for dispatching the special purpose mapping vehicle for these updates. The captured low-resolution data can be compared with the high-resolution map portion to determine that the sensor data reflects a feature in a way that is inconsistent with how that feature is represented in the authoritative map portion. The sensor data can then be used to relabel the authoritative map portion.

Abstract: Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.

Abstract: The present technology provides a system that can update aspects of an authoritative map portion stored on an autonomous vehicle using low-resolution data from the at least one sensor of the autonomous vehicle, and therefore avoids the need for dispatching the special purpose mapping vehicle for these updates. The captured low-resolution data can be compared with the high-resolution map portion to determine that the sensor data reflects a feature in a way that is inconsistent with how that feature is represented in the authoritative map portion. The sensor data can then be used to relabel the authoritative map portion.

Abstract: Systems and methods are disclosed for detecting crosswalk locations. Crosswalk locations can be detected by determining at least two painted lines on a road surface, and then combining the at least two painted lines on the road surface into a grouping of related elements. The grouping of related elements can be classified into a crosswalk (or in some embodiments a type of crosswalk) based on attributes of the grouping of related elements, where the attributes of the grouping of the elements includes a distance between the at least two painted lines, and an orientation of the grouping of related elements on the road surface.

Abstract: The present technology provides systems, methods, and devices that can dynamically augment aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle to keep navigating a route. As the autonomous vehicle navigates a route, the autonomous vehicle can determine that current data captured by at least one sensor of the autonomous vehicle describing a location is inconsistent with the primary map of the location. The autonomous vehicle can determine that a portion of the current data describes a second feature that is distinct from a first feature described by a primary map. The second feature can be added to an augmented map that is based on the primary map, and the position of the autonomous vehicle can be located with respect to the first feature rather than the second feature on the augmented map.

Abstract: The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. A type of the new feature can be classified in accordance with an analysis of a structure of the current data that has been clustered together based on a threshold spatial closeness, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

Abstract: The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. The current data can be clustered together based on a threshold spatial closeness, where the clustering describes the new feature, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

Abstract: The present technology provides a system that can update aspects of an authoritative map portion stored on an autonomous vehicle using low-resolution data from the at least one sensor of the autonomous vehicle, and therefore avoids the need for dispatching the special purpose mapping vehicle for these updates. The captured low-resolution data can be compared with the high-resolution map portion to determine that the sensor data reflects a feature in a way that is inconsistent with how that feature is represented in the authoritative map portion. The sensor data can then be used to relabel the authoritative map portion.

Abstract: Systems and method are provided for localizing a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from a sensor of the vehicle; filtering, by the processor, the sensor data for data associated with static elements of the environment; determining, by the processor, realtime height values associated with the static elements; correlating, by the processor, the realtime height values with defined height values associated with a map of the environment; localizing, by the processor, the vehicle on the map based on the correlated realtime height values and the defined height values; and controlling, by the processor, the vehicle based on the localizing.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving semantic map data, via a processor, wherein the semantic map data includes traffic light location data, calculating route data using the semantic map data, via a processor; viewing, via a sensing device, a traffic light and assessing a state of the viewed traffic light, via a processor, based on the traffic light location data, and controlling driving of an autonomous vehicle based at least on the route data and the state of the traffic light, via a processor.

Abstract: Systems and method are provided for localizing a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from a sensor of the vehicle; filtering, by the processor, the sensor data for data associated with static elements of the environment; determining, by the processor, realtime height values associated with the static elements; correlating, by the processor, the realtime height values with defined height values associated with a map of the environment; localizing, by the processor, the vehicle on the map based on the correlated realtime height values and the defined height values; and controlling, by the processor, the vehicle based on the localizing.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving semantic map data, via a processor, wherein the semantic map data includes traffic light location data, calculating route data using the semantic map data, via a processor; viewing, via a sensing device, a traffic light and assessing a state of the viewed traffic light, via a processor, based on the traffic light location data, and controlling driving of an autonomous vehicle based at least on the route data and the state of the traffic light, via a processor.

Abstract: A method for image-based vehicle localization includes measuring, at a vehicle, a position and a heading of the vehicle; capturing, at an image capture system of the vehicle, road surface image data of a road surface; processing the road surface image data to correct for distortion in the road surface image data due to pitch and roll of the vehicle; performing feature detection on the processed road surface image data to detect lane markers on the road surface; generating a local map based on the detected lane markers; wherein generating the local map comprises identifying a lane demarcated by the detected lane markers; and controlling the vehicle according to the local map.

Abstract: A method for image-based vehicle localization includes measuring, at a vehicle, a position and a heading of the vehicle; capturing, at an image capture system of the vehicle, road surface image data of a road surface; processing the road surface image data to correct for distortion in the road surface image data due to pitch and roll of the vehicle; performing feature detection on the processed road surface image data to detect lane markers on the road surface; generating a local map based on the detected lane markers; wherein generating the local map comprises identifying a lane demarcated by the detected lane markers; and controlling the vehicle according to the local map.