Determining autonomous vehicle status based on mapping of crowdsourced object data
A map in a cloud service stores physical objects previously detected by other vehicles that have previously traveled over the same road that a current vehicle is presently traveling on. New data received by the cloud service from the current vehicle regarding new objects that are being encountered by the current vehicle can be compared to the previous object data stored in the map. Based on this comparison, an operating status of the current vehicle is determined. In response to determining the status, an action such as terminating an autonomous navigation mode of the current vehicle is performed.
1. A method comprising:
receiving, by at least one processor of a server, data regarding objects detected by a plurality of vehicles, the detected objects including a first object;
storing, by the at least one processor, a map comprising the detected objects, each object having an object type and a location;
subsequent to receiving the data regarding objects detected by the plurality of vehicles, receiving, by the server over a communication network, first data regarding objects detected by a first vehicle, the first data including a location of the first object detected by the first vehicle;
determining, by the server based on comparing the received first data to the map, that the first vehicle has failed to detect the first object, wherein determining that the first vehicle has failed to detect the first object includes determining that the location of the first object detected by the first vehicle does not match within a tolerance of the location of the first object stored in the map; and
in response to determining that the first vehicle has failed to detect the first object, performing an action associated with operation of the first vehicle.
2. The method of claim 1 , wherein the first object is a traffic sign, a traffic light, a road lane, or a physical structure.
3. The method of claim 1 , further comprising determining a location of the first vehicle, wherein determining that the first vehicle has failed to detect the first object further includes comparing the location of the first vehicle to the location of the first object stored in the map.
4. The method of claim 1 , wherein the first vehicle is a vehicle other than the plurality of vehicles.
5. The method of claim 1 , further comprising analyzing the first data, wherein performing the action comprises configuring, based on analyzing the first data, at least one action performed by the first vehicle.
6. The method of claim 1 , wherein the first vehicle is an autonomous vehicle comprising a controller and a storage device, wherein the action comprises updating firmware of the controller, and wherein the updated firmware is stored in the storage device.
7. The method of claim 1 , further comprising training a computer model using at least one of supervised or unsupervised learning, wherein the training is done using data collected from the plurality of vehicles, and wherein determining that the first vehicle has failed to detect the first object is based at least in part on an output from the computer model.
8. The method of claim 1 , wherein the first data comprises image data obtained from at least one sensor of the first vehicle.
9. The method of claim 1 , further comprising analyzing the first data, wherein the first data comprises image data, and analyzing the first data comprises performing pattern recognition using the image data to determine a type of object detected by the first vehicle.
10. The method of claim 1 , further comprising providing the first data as an input to an artificial neural network model, wherein the action performed is based on an output from the artificial neural network model.
11. A system comprising:
at least one processor; and
memory storing instructions configured to instruct the at least one processor to:
receive data regarding objects, each object detected by at least one of a plurality of vehicles, and the detected objects including a first object;
store, based on the received data, a map including the detected objects, each of the detected objects associated with a respective location;
receive first data regarding at least one object detected by a first vehicle;
compare a location of an object detected by the first vehicle to a location of the first object;
determine, based on comparing the received first data to the map, that the first vehicle has failed to detect the first object, wherein determining that the first vehicle has failed to detect the first object is based at least in part on comparing the location of the object detected by the first vehicle to the location of the first object; and
in response to determining that the first vehicle has failed to detect the first object, perform at least one action, wherein performing the at least one action comprises sending a communication to the first vehicle, the communication causing the first vehicle to perform at least one of deactivating an autonomous driving mode of the first vehicle or activating a backup navigation device of the first vehicle.
12. The system of claim 11 , wherein performing the at least one action further comprises sending at least one communication to a computing device other than the first vehicle.
13. The system of claim 12 , wherein the computing device is a server that monitors a respective operating status for each of the plurality of vehicles.
14. The system of claim 12 , wherein the instructions are further configured to instruct the at least one processor to determine that an accident involving the first vehicle has occurred, and wherein the at least one communication to the computing device comprises data associated with operation of the first vehicle prior to the accident.
15. The system of claim 11 , wherein the received data regarding objects detected by the plurality of vehicles includes data collected by a plurality of sensors for each of the vehicles.
16. The system of claim 11 , wherein performing the at least one action is based on an output from a machine learning model, and wherein the machine learning model is trained using training data, the training data comprising data collected by sensors of the plurality of vehicles.
17. A non-transitory computer storage medium storing instructions which, when executed on a computing device, cause the computing device to perform a method comprising:
receiving data regarding objects detected by a plurality of vehicles;
storing a map including respective locations for each of the detected objects;
subsequent to receiving the data regarding objects detected by the plurality of vehicles, receiving first data regarding at least one object detected by a first vehicle, the first data comprising location data for the at least one object;
determining, based on comparing the received first data to the map, that the first data fails to match data for at least one object stored in the map, wherein determining that the first data fails to match data for at least one object stored in the map includes determining that a location of a first object detected by the first vehicle does not match the location of the first object stored in the map; and
in response to determining that the first data fails to match data for at least one object stored in the map, causing an update of firmware for a controller of the first vehicle.
18. The non-transitory computer storage medium of claim 17 , wherein the first data comprises data obtained from an artificial neural network model of the first vehicle.