Abstract: Provided herein is a system on a vehicle, the system comprising one or more sensors, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: receiving one or more ride requests for ridesharing from one or more users; receiving respective preferences from each of the one or more users; selecting a ride request of a user from the one or more ride requests based on the respective preferences; notifying the user of the selecting of the ride request; sending at least one of the images or videos of the interior of the vehicle to the user; in response to the sending, determining whether the user confirms the ride request; and in response to determining that the user confirms the ride request, selecting a route to the user and driving, according to the route, to the user.

Abstract: An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine information of an acoustic emission from the compressor and to counteract the acoustic emission based on the determined information.

Abstract: Described herein are systems, methods, and non-transitory computer readable media for using 3D point cloud data such as that captured by a LiDAR as ground truth data for training an instance segmentation deep learning model. 3D point cloud data captured by a LiDAR can be projected on a 2D image captured by a camera and provided as input to a 2D instance segmentation model. 2D sparse instance segmentation masks may be generated from the 2D image with the projected 3D data points. These 2D sparse masks can be used to propagate loss during training of the model. Generation and use of the 2D image data with the projected 3D data points as well as the 2D sparse instance segmentation masks for training the instance segmentation model obviates the need to generate and use actual instance segmentation data for training, thereby providing an improved technique for training an instance segmentation model.

Abstract: Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a sensor, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform, determining data of interest comprising an object, feature, or region of interest, determining whether a sharpness of the data of interest exceeds a threshold, in response to determining that the sharpness does not exceed a threshold, operating the sensor to increase the sharpness of the data of interest until the sharpness exceeds the threshold, in response to the sharpness exceeding the threshold, determining a driving action of a vehicle based on the data of interest, and performing the driving action.

Abstract: A computing component implemented as part of a vehicle architecture and configured to process point cloud data. The computing component comprising one or more programmable logics that, when executed, cause the computing component to generate one or more transformations to align scans of point cloud data, translate the aligned scans of the point cloud data to a universal coordinate system, and generate addresses and offsets to store the aligned scans of the point cloud data.

Abstract: Provided herein is a power distribution system comprising a main power bus, sub-buses coupled to the main power bus, and a controller. The sub-buses provide power to electrical components of a vehicle. Each of the sub-buses includes an electrically programmable fuse in series with a relay. The controller is configured to detect a fault in a sub-bus of the sub-buses, determine a fault type associated with the fault, and in response to determining the fault type, generate a command to cause the relay to change a relay state.

Abstract: Systems and methods are provided for cooling air in a vehicle. The system includes a chassis, inside an interior area of a vehicle, with one or more openings that are configured to allow air to enter the chassis to cool a heat generating component in the vehicle and an exhaust duct that directs the air away from the chassis after the air has contacted at least a portion of the heat generating component. The system includes a fan that acts to propel the air through the one or more openings and through the exhaust duct.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

Abstract: Systems, methods, and non-transitory computer-readable media configured to obtain one or more series of successive sensor data frames during a navigation of a vehicle. Disengagement data is obtained. The disengagement data indicates whether a vehicle is in autonomous mode. A training dataset with which train a machine learning model is determined based on the one or more series of successive sensor data frames and the disengagement data. The training dataset includes a subset of the one or more series of successive sensor data frames and a subset of the disengagement data, the machine learning model being trained to identify unsafe driving conditions.

Abstract: A computing system is implemented as part of a vehicle architecture. The computing system includes a computing component, a first computing node that includes a power distribution system, a second computing node that includes input/output (I/O) interfaces to connect to devices, actuators, or sensors, and a third computing node. The computing component further comprises one or more processors and instructions or logic that, when executed by the one or more processors, cause the computing component to perform, transmitting commands to the second computing node, the commands associated with initial processing of data received at the second computing node, receiving initially processed data from the second computing node, and performing further processing on the initially processed data.

Abstract: Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

Abstract: An apparatus includes a processing node of a distributed computing platform. The processing node communicates with other processing nodes over one or more networks. The processing node may receive frames of point clouds at a processing node of a distributed computing platform, determine a subset of the frames as key frames based at least in part on distances travelled between captures of the respective frames, and allocate tasks of processing the key frames to processing subnodes based at least in part on estimated processing demands of the key frames and processing capabilities of each of the processing subnodes.

Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. Static objects present in the environment are detected such as signage. Point cloud data representative of the static objects are captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by application of a second known transformation matrix to transition from the local coordinate system of the second pre-calibrated sensor to the global coordinate system.

Abstract: Described herein are systems, methods, and non-transitory computer-readable media for self-detection of a fault condition by a vehicle component, generation of a device health code that includes multiple tiers of information relating to the fault condition experienced by the vehicle component, and broadcasting of the device health code to one or more other vehicle components via one or more vehicle communication networks. A recommended vehicle response measure indicated by a reaction code in the device health code can then be taken or alternate vehicle response may be selected and initiated based on an evaluation of current vehicle operational data.

Abstract: An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine a current velocity of the vehicle and control an operation of the compressor based on the current velocity of the vehicle.

Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. A static object present in the environment is detected such as signage. A type of the detected object is determined from static map data. Point cloud data representative of the static object is captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by a second known transformation matrix.

Abstract: Provided herein is a system and method for a sensor system on a vehicle. The sensor system comprises sensors connected with one another in a daisy chain communication network. The sensor system further comprises a controller connected to at least one of the sensors. The controller is configured to operate the vehicle based on data from the sensors and to operate the daisy chain communication network.

Abstract: An environmental safety system may comprise a plurality of first sensors each located at a predetermined physical location of a traffic intersection and with a predetermined orientation. The system may have a memory storing executable instructions. The system may have one or more processors in communication with the plurality of first sensors and the memory. The one or more processors may be programmed by the executable instructions. The system may receive first sensor data captured at a time point and by the plurality of first sensors. The system may determine values of one or more parameters of an object of interest within a threshold distance of the traffic intersection using the first sensor data. The system may generate an information object comprising the values of the one or more parameters of the object of interest, the time point, and a signature of the information object. The system may transmit, via a communication network, the information object.

Abstract: Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a processor configured to acquire data of nonuniform resolution over a field of view of the sensor, and a controller configured to determine a driving action of a vehicle based on the data, and perform the driving action.