A method and apparatus for defining a model to determine a corrected trajectory of a mobile device or vehicle and a method and apparatus for determined a corrected trajectory using a defined model are provided. The model for determining a corrected trajectory includes accessing ground truth location data for a selected pathway, determining a GNSS pathway of a mobile device or vehicle, determining an IMU pathway of a mobile device or vehicle, and calculating an aggregated displacement trajectory. The apparatus for defining the model includes a communication interface configured to receive a first and second pathway, a memory configured to store a model and ground truth location data, and a processor to train the model.

An example operation includes one or more of determining one or more objects in a transport that may become airborne and altering a portion of the transport to minimize the one or more objects from becoming airborne during a collision.

A method for determining hard example sensor data inputs for training a task neural network is described. The task neural network is configured to receive a sensor data input and to generate a respective output for the sensor data input to perform a machine learning task. The method includes: receiving one or more sensor data inputs depicting a same scene of an environment, wherein the one or more sensor data inputs are taken during a predetermined time period; generating a plurality of predictions about a characteristic of an object of the scene; determining a level of inconsistency between the plurality of predictions; determining that the level of inconsistency exceeds a threshold level; and in response to the determining that the level of inconsistency exceeds a threshold level, determining that the one or more sensor data inputs comprise a hard example sensor data input.

An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database. The approach further involves providing the correlation as an output.

An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

Systems and methods are disclosed herein for monitoring a location of a client device associated with a transportation service and generating augmented reality images for display on the client device. The systems and methods use sensor data from the client device and a device localization process to monitor the location of the client device by comparing renderings of images captured by the client device to renderings of the vicinity of the pickup location. The systems and methods determine navigation instructions from the user's current location to the pickup location and select one or more augmented reality elements associated with the navigation instructions and/or landmarks along the route to the pickup location. The systems and methods instruct the client device to overlay the selected augmented reality elements on a video feed of the client device.

Systems and methods are disclosed herein for monitoring a location of a client device associated with a transportation service and generating augmented reality images for display on the client device. The systems and methods use sensor data from the client device and a device localization process to monitor the location of the client device by comparing renderings of images captured by the client device to renderings of the vicinity of the pickup location. The systems and methods determine navigation instructions from the user's current location to the pickup location and select one or more augmented reality elements associated with the navigation instructions and/or landmarks along the route to the pickup location. The systems and methods instruct the client device to overlay the selected augmented reality elements on a video feed of the client device.

Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.

Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.