Abstract: In some examples, a system receives sensor data from sensors on board a vehicle. Based at least on the sensor data indicating an anomaly has occurred and is affecting a travel path of the vehicle, an uncertainty threshold is compared with an uncertainty value associated with a prediction model. For instance, the uncertainty value may be indicative of a difference between a predicted value predicted by the prediction model and an actual measured value related to at least one operation parameter of the vehicle. Based at least on determining that the anomaly has occurred and that the uncertainty value is within the uncertainty threshold, sending, by the system, a communication for obtaining an updated prediction model.

Abstract: In some examples, a service computing device receives, from a vehicle computing device onboard a vehicle, a communication including sensor data obtained from at least one sensor onboard the vehicle. Based on the communication, the service computing device determines a transmission delay corresponding to a difference between a time when the vehicle sent the communication and a time when the communication was received. Based on the communication, the service computing device determines a trajectory of the vehicle on a travel route. Based on the sensor data, transmission delay, wind direction and magnitude, surrounding objects, and trajectory, the service computing device determines vehicle control information for the vehicle. The service computing device sends, to the vehicle, based at least on the vehicle control information, at least one instruction causing the vehicle to perform at least one control operation for traversing one or more locations associated with the trajectory of the vehicle.

Abstract: In some examples, a service computing device receives, from a vehicle computing device onboard a vehicle, a communication including sensor data obtained from at least one sensor onboard the vehicle. Based on the communication, the service computing device determines a transmission delay corresponding to a difference between a time when the vehicle sent the communication and a time when the communication was received. Based on the communication, the service computing device determines a trajectory of the vehicle on a travel route. Based on the sensor data, transmission delay, wind direction and magnitude, surrounding objects, and trajectory, the service computing device determines vehicle control information for the vehicle. The service computing device sends, to the vehicle, based at least on the vehicle control information, at least one instruction causing the vehicle to perform at least one control operation for traversing one or more locations associated with the trajectory of the vehicle.

Abstract: A system comprising at least one hardware processor for updating a cloud-based road anomaly database is disclosed, wherein the system receives information from a vehicle regarding an object detected by one or more sensors of the vehicle, the information may include a location and/or a size of the detected object; compares the location and/or the size of the first detected object against data stored in a road feature database regarding the first detected object; determines, based on comparing the location and/or the size, an accuracy score associated with the first detected object and the second detected object; and updates the cloud-based road anomaly database with the received information for the second detected object based on the associated accuracy score being higher than at least one of a threshold accuracy score or an accuracy score associated with corresponding object information stored in the cloud-based road anomaly database.

Abstract: In some examples, a system may determine a plurality of candidate routes between a source location and a destination location for a vehicle, and may segment each candidate route into multiple road segments. Further, the system may receive vehicle computing resource information and sensor configuration information for the vehicle. The system may determine that at least one of the sensor configuration or the computing resources on board the vehicle fails to satisfy a threshold associated with autonomous navigation of a road segment of a first candidate route of the plurality of candidate routes. The system may select, for the vehicle, the first candidate route based at least on determining that a computing device external to the vehicle is scheduled to perform at least one computational task for the vehicle to enable the vehicle to meet the threshold associated with autonomously navigating the road segment of the first candidate route.

Abstract: In some examples, a system may determine a plurality of candidate routes to a destination location for a vehicle, and may segment each candidate route into multiple road segments. The system determines one or more compression methods to be utilized for compressing images captured by one or more vehicle cameras on board the vehicle while the vehicle is traversing at least one road segment of at least one route of the plurality of candidate routes. The system selects, for the vehicle, a first route based at least on the one or more compression methods. The system sends, to the vehicle, information related to the first route and the one or more compression methods. The vehicle may utilize the one or more compression methods for compressing the images captured by the one or more vehicle cameras during traversal of the first route.

Abstract: In some examples, a system may determine route information for a route to be traversed by a vehicle from a start location to a destination location, the vehicle including a battery having a state of health. The system receives discharge data corresponding to traversal of the vehicle along the route, the discharge data indicative of a rate of discharge of the battery at a plurality of locations along the route. Based on the received discharge data, the system at least one of trains or updates a first machine learning model configured for predicting the state of health of the battery. The vehicle may determine an estimated battery state of health based at least on the first machine learning model, and may receive control information while traversing the route based on the estimated battery state of health to at least partially minimize battery degradation during traversal of the route.

Abstract: In some examples, a system may determine a plurality of candidate routes between a source location of a vehicle and a destination location for the vehicle. Further, the system may determine one or more respective observation zone volumes for each candidate route of the plurality of candidate routes. The system may determine a field of view (FOV) for one or more vehicle sensors onboard the vehicle. In addition, the system may select a route for the vehicle from the plurality of candidate routes based at least on comparing an overlap of the FOV with the respective observation zone volumes for the candidate routes.

Abstract: In some examples, a vehicle is able to communicate with a computing device over a network. The vehicle includes a processor configured to determine a future driving condition for the vehicle, and further determine that the future driving condition corresponds to a past accident condition. The processor receives, from the computing device, verification data for at least one diagnostic pattern corresponding to the past accident condition. The processor executes an artificial intelligence (AI) algorithm using the verification data corresponding to the at least one diagnostic pattern. Based on determining that an output of the AI algorithm does not correspond to an expected output for the verification data, the processor performs at least one action.

Abstract: Example implementations described herein involve systems and method which can include receiving, from a connected automated electric vehicle (CAEV), vehicle information related to operation of the CAEV; determining one or more candidate routes to a destination of the CAEV based at least on the vehicle information; determining whether the CAEV is on a road segment of the one or more candidate routes to the destination having a dynamic charging system; and sending, to the CAEV, a path planning trajectory while identifying a localization accuracy of one or more sensors of the CAEV to update the localization accuracy of the CAEV based on a battery of the CAEV being charged with the dynamic charging system along the one or more candidate routes.

Abstract: In some examples, a system receives sensor data from sensors on board a vehicle. Based at least on the sensor data indicating an anomaly has occurred and is affecting a travel path of the vehicle, an uncertainty threshold is compared with an uncertainty value associated with a prediction model. For instance, the uncertainty value may be indicative of a difference between a predicted value predicted by the prediction model and an actual measured value related to at least one operation parameter of the vehicle. Based at least on determining that the anomaly has occurred and that the uncertainty value is within the uncertainty threshold, sending, by the system, a communication for obtaining an updated prediction model.

Abstract: In some examples, a system may determine a plurality of candidate routes between a source location and a destination location for a vehicle, and may segment each candidate route into multiple road segments. Further, the system may receive vehicle computing resource information and sensor configuration information for the vehicle. The system may determine that at least one of the sensor configuration or the computing resources on board the vehicle fails to satisfy a threshold associated with autonomous navigation of a road segment of a first candidate route of the plurality of candidate routes. The system may select, for the vehicle, the first candidate route based at least on determining that a computing device external to the vehicle is scheduled to perform at least one computational task for the vehicle to enable the vehicle to meet the threshold associated with autonomously navigating the road segment of the first candidate route.

Abstract: In some examples, a system may receive, from at least one camera of a vehicle, at least one image including a road. The system may further receive vehicle location information including an indication of a location of the vehicle. In addition, the system may receive at least one of historical information from a historical database, or road anomaly information, where the road anomaly information is determined from at least one of a road anomaly database or real-time road anomaly detection. Based on the at least one image, the indication of the location of the vehicle, and the at least one of the historical information or the road anomaly information, the system may generate at least one of a disparity map or a disparity image.

Abstract: In some examples, a rotary electric machine includes a stator having a plurality of stator coils arranged in a circular pattern around a central opening configured for receiving a rotor. The rotary electric machine further includes a respective dedicated inverter circuit associated with each respective stator coil. For instance, each respective inverter circuit may be configured to convert direct current power to alternating current power to provide to the respective stator coil.

Abstract: In some examples, one or more processors may receive at least one first visible light image and a first thermal image. Further, the processor(s) may generate, from the at least one first visible light image, an edge image that identifies edge regions in the at least one first visible light image. At least one of a lane marker or road edge region may be determined based at least in part on information from the edge image. In addition, one or more first regions of interest in the first thermal image may be determined based on at least one of the lane marker or the road edge region. Furthermore, a gain of a thermal sensor may be adjusted based on the one or more first regions of interest in the first thermal image.

Abstract: A system comprising at least one hardware processor for updating a cloud-based road anomaly database is disclosed, wherein the system receives information from a vehicle regarding an object detected by one or more sensors of the vehicle, the information may include a location and/or a size of the detected object; compares the location and/or the size of the first detected object against data stored in a road feature database regarding the first detected object; determines, based on comparing the location and/or the size, an accuracy score associated with the first detected object and the second detected object; and updates the cloud-based road anomaly database with the received information for the second detected object based on the associated accuracy score being higher than at least one of a threshold accuracy score or an accuracy score associated with corresponding object information stored in the cloud-based road anomaly database.

Abstract: In some examples, a system may determine a plurality of candidate routes between a source location of a vehicle and a destination location for the vehicle. Further, the system may determine one or more respective observation zone volumes for each candidate route of the plurality of candidate routes. The system may determine a field of view (FOV) for one or more vehicle sensors onboard the vehicle. In addition, the system may select a route for the vehicle from the plurality of candidate routes based at least on comparing an overlap of the FOV with the respective observation zone volumes for the candidate routes.

Abstract: In some examples, a system may determine a plurality of routes between a source location and a destination location. Further, the system may segment each route of the plurality of routes into multiple road segments. The system may determine a first field of view (FOV) for each road segment. In addition, the system may receive vehicle sensor configuration information for vehicle sensors on board a vehicle. The system may determine a second FOV for the vehicle sensors. Additionally, the system may select a route for the vehicle based at least on comparing the second FOV with the first FOV for a plurality of the road segments.

Abstract: In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

Abstract: A system for dynamic image compression in autonomous vehicles with multiple cameras. The cameras of a vehicle may be prioritized based on various features, and different image compression techniques may be assigned to different cameras in the vehicle, based on the priorities of the cameras, to reduce overall processing load of the vehicle.