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: Systems and methods of predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

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 automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive an indication of a traffic condition at an autonomous vehicle hub; upon receiving the indication of the traffic condition at the autonomous vehicle hub, identify an autonomous vehicle traveling a route that includes the autonomous vehicle hub; generate a control command for the autonomous vehicle based on the route and the traffic condition; and transmit the control command to the autonomous vehicle to correct the traffic condition at the autonomous vehicle hub.

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 predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

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: A path determination system is provided. The path determination system includes a processor and a memory. The processor is configured to receive sensor data from one or more sensors of an autonomous vehicle, identify, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects, determine the one or more objects form a barrier indicating closure of all of the one or more forward travel lanes, when the barrier indicates closure of all of the one or more forward travel lanes, determine an alternative travel path for the autonomous vehicle, and control the autonomous vehicle to travel the alternative travel path.

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: 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 automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive an indication of a traffic condition at an autonomous vehicle hub; upon receiving the indication of the traffic condition at the autonomous vehicle hub, identify an autonomous vehicle traveling a route that includes the autonomous vehicle hub; generate a control command for the autonomous vehicle based on the route and the traffic condition; and transmit the control command to the autonomous vehicle to correct the traffic condition at the autonomous vehicle hub.

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 predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

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 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 automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive, from a first autonomous vehicle traveling on a road, a first correction to map data identifying a location in the map data; generate a modified parameter of the map data based on the first correction and a second correction identifying the location, the second correction received from a second autonomous vehicle traveling on the road; update the map data based on the modified parameter; and provide the updated map data to the first autonomous vehicle.