Abstract: An autonomy computing system of an autonomous vehicle for generating a virtual fence. The autonomy computing system receives data of a plurality of temporary barriers on a road based upon analysis of sensor data from one or more sensors of the autonomous vehicle, determines a closure intent associated with the plurality of temporary barriers exists, constructs a virtual fence associated with the plurality of temporary barriers, the virtual fence delineating a section of the road in which an autonomous vehicle is restricted from driving; and operates the autonomous vehicle based on the virtual fence.

Abstract: A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: A method comprises receiving a first set of data associated with a plurality of retroreflective features from a vehicle; retrieving a digital map comprising vectorized data associated with the plurality of retroreflective features and a location associated with the plurality of retroreflective features, the map previously generated based on monitoring a second set of data retrieved from a sensor of a second vehicle where the second set of data is associated with the plurality of retroreflective features near a road being driven by the second vehicle; generating a score for each retroreflective feature indicating a match between each retroreflective feature as indicated within the digital map and a location of each retroreflective feature as identified within the first set of data; and localizing the vehicle.

Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

Abstract: Systems and methods for path planning for autonomous vehicles err on the side of a safer road position and safer type of collision if one were to occur. An autonomous vehicle perception module detects target objects within a region of interest (ROI). An autonomous vehicle processor estimates relative velocity between the ego vehicle and target objects, and estimates mass of target objects. Collision-aware path planning calculates a cost function that assigns higher costs for paths that take the vehicle close to objects that have a large velocity difference and higher costs for objects with large estimated mass. Path planning provides a cost map that yields a path that has appropriate buffer distances between the autonomous vehicle and surrounding objects. In the event the ego vehicle balances buffer distance margins to multiple surrounding target objects, target objects that would create more severe collisions are given greater buffer distance.

Abstract: Disclosed herein are methods and systems (sensors) that allow revision of HD maps for lane lines as the lane lines are created. In a non-limiting example, a method comprises receiving, by a processor from a sensor associated with a vehicle configured to paint a lane line, a color attribute, a patterns attribute, and a location of a lane line painted on a road by the vehicle; determining, by the processor, using the location received from the sensor, whether the road has an existing lane line; and revising, by the processor, a high definition map associated with at least one autonomous vehicle by removing the existing lane line from the high definition map and inserting the lane line in the high definition map.

Abstract: Disclosed herein are methods and systems (sensors) that allow revision of HD maps for lane lines as the lane lines are created. In a non-limiting example, a method comprises receiving, by a processor from a sensor associated with a vehicle configured to paint a lane line, a color attribute, a patterns attribute, and a location of a lane line painted on a road by the vehicle; determining, by the processor, using the location received from the sensor, whether the road has an existing lane line; and revising, by the processor, a high definition map associated with at least one autonomous vehicle by removing the existing lane line from the high definition map and inserting the lane line in the high definition map.

Abstract: A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.

Abstract: Disclosed herein are methods and systems (sensors) that allow revision of HD maps for lane lines as the lane lines are created. In a non-limiting example, a method comprises receiving, by a processor from a sensor associated with a vehicle configured to paint a lane line, a color attribute, a patterns attribute, and a location of a lane line painted on a road by the vehicle; determining, by the processor, using the location received from the sensor, whether the road has an existing lane line; and revising, by the processor, a high definition map associated with at least one autonomous vehicle by removing the existing lane line from the high definition map and inserting the lane line in the high definition map.

Abstract: Disclosed herein are methods and systems to provide intelligent display for vehicles including a method that comprises determining data associated a road sign; retrieving driving data associated with a vehicle; generating, using the driving data and data associated with the road sign, a graphical overlay presenting a virtual object corresponding to the trajectory of the vehicle or data associated with the vehicle; and presenting on an electronic device associated with the vehicle, the graphical overlay while the electronic device is presenting an image of a surrounding of the vehicle, wherein the graphical overlay is superimposed upon an image of the road sign.

Abstract: Disclosed herein are methods and systems to provide intelligent display for vehicles including a method that comprises determining, by a processor, that a current trajectory of an autonomous vehicle includes a revised road condition having at least one attribute; retrieving, by the processor using the at least one attribute, a graphical overlay corresponding to the revised road condition; and displaying, by the processor on an electronic device associated with the autonomous vehicle, the graphical overlay while the electronic device is presenting an image of a surrounding of the autonomous vehicle.

Abstract: A method comprises receiving a first set of data associated with a plurality of retroreflective features from a vehicle; retrieving a digital map comprising vectorized data associated with the plurality of retroreflective features and a location associated with the plurality of retroreflective features, the map previously generated based on monitoring a second set of data retrieved from a sensor of a second vehicle where the second set of data is associated with the plurality of retroreflective features near a road being driven by the second vehicle; generating a score for each retroreflective feature indicating a match between each retroreflective feature as indicated within the digital map and a location of each retroreflective feature as identified within the first set of data; and localizing the vehicle.

Abstract: Systems and methos for learning safe driving paths in autonomous driving adversity conditions can include acquiring, by a computer system, vehicle sensor data for a plurality of driving events associated with one or more autonomous driving adversity conditions. The vehicle sensor data can include, for each driving event, corresponding image data depicting surroundings of the vehicle and corresponding inertial measurement data. The computer system can acquire, for each driving event of the plurality of driving events, corresponding vehicle positioning data indicative of a corresponding trajectory followed by the vehicle, and train, using the vehicle sensor data and the vehicle positioning data, a machine learning model to predict navigation trajectories during the autonomous driving adversity conditions.

Abstract: Systems and methods for generating safe driving paths in autonomous driving adversity conditions can include receiving, by a computer system including one or more processors, sensor data of a vehicle that is traveling. The sensor data can be indicative of one or more autonomous driving adversity conditions, and can include image data depicting surroundings of the vehicle. The method can include executing, by the computer system, a trained machine learning model to predict, using the sensor data, a trajectory to be followed by the vehicle through the one or more autonomous driving adversity conditions, and providing, by the computer system, an indication of the predicted trajectory to an autonomous driving system of the vehicle.

Abstract: A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.

Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

Abstract: Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.