Autonomous and user controlled vehicle summon to a target
A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.
1. A system, comprising:
one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle and one or more processors configured to:
obtain information indicative of a location to which the vehicle is to navigate;
generate a representation of the real-world environment surrounding the vehicle based on a machine learning model and the sensor data, by:
determining a plurality of drivable spaces and a plurality of non-drivable spaces surrounding the vehicle; and
generating an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces,
wherein the occupancy grid includes one or more drivability values at each grid location of a plurality of grid locations, and wherein a drivability value at a grid location represents a numerical probability that a corresponding location in the real-world environment is drivable and is selected from a plurality of drivability values within a range of numerical drivability values;
cause the vehicle to navigate based on a path associated with navigating to the location, wherein the path is calculated based on the representation of the real-world environment;
as the vehicle navigates, obtain sensor data and update the drivability values included in the occupancy grid; and
adjust the path based on at least one updated drivability value.
2. The system of claim 1 , wherein the location is selected via a user interface of an application configured to execute on a mobile device.
3. The system of claim 1 , wherein the location is based on a global positioning system location associated with a mobile device.
4. The system of claim 3 , wherein during navigation the location is updated based on the location associated with the mobile device.
5. The system of claim 1 , wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device.
6. The system of claim 1 , wherein the one or more sensors consist of cameras.
7. The system of claim 1 , wherein the one or more sensors comprise one or more of a camera, a radar, or an ultrasonic sensor.
8. The system of claim 7 , wherein the one or more sensors comprise a lidar.
9. The system of claim 1 , wherein the path is calculated based on one or more cost metrics associated with the drivability values.
10. The system of claim 9 , wherein a plurality of paths are calculated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths.
11. A method implemented by a system of one or more processors, the system in communication with one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle, and the method comprising:
obtaining information indicative of a location to which the vehicle is to navigate;
generating a representation of the real-world environment surrounding the vehicle based on a machine learning model and the sensor data, by:
determining a plurality of drivable spaces and a plurality of non-drivable spaces surrounding the vehicle; and
generating an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces,
wherein the occupancy grid includes one or more drivability values at each grid location of a plurality of grid locations, and wherein a drivability value at a grid location represents a numerical probability that a corresponding location in the real-world environment is drivable and is selected from a plurality of drivability values within a range of numerical drivability values;
causing the vehicle to navigate based on a path associated with navigating to the location, wherein the path is calculated based on the representation of the real-world environment;
as the vehicle navigates, obtaining sensor data and update the drivability values included in the occupancy grid; and
adjusting the path based on at least one updated drivability value.
12. The method of claim 11 , wherein the location is selected via a user interface of an application configured to execute on a mobile device.
13. The method of claim 11 , wherein the location is based on a global positioning system location associated with a mobile device.
14. The method of claim 13 , wherein during navigation the location is updated based on the location associated with the mobile device.
15. The method of claim 11 , wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device.
16. The method of claim 11 , wherein the one or more sensors consist of cameras.
17. The method of claim 11 , wherein the one or more sensors comprise one or more of a camera, a radar, or an ultrasonic sensor.
18. The method of claim 17 , wherein the one or more sensors comprise a lidar.
19. The method of claim 11 , wherein the path is calculated based on one or more cost metrics associated with the drivability values, and wherein a plurality of paths are calculated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths.
20. Non-transitory computer storage media storing instructions that when executed by a system one or more processors, cause the one or more processors to perform operations, wherein the system is in communication with one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle, and wherein the operations comprise:
obtaining information indicative of a location to which the vehicle is to navigate;
generating a representation of the real-world environment surrounding the vehicle based on a machine learning model and the sensor data, by:
determining a plurality of drivable spaces and a plurality of non-drivable spaces surrounding the vehicle; and
generating an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces,
wherein the occupancy grid includes one or more drivability values at each grid location of a plurality of grid locations, and wherein a drivability value at a grid location represents a numerical probability that a corresponding location in the real-world environment is drivable and is selected from a plurality of drivability values within a range of numerical drivability values;
causing the vehicle to navigate based on a path associated with navigating to the location, wherein the path is calculated based on the representation of the real-world environment;
as the vehicle navigates, obtaining sensor data and update the drivability values included in the occupancy grid; and
adjusting the path based on at least one updated drivability value.