Pony AI Inc.

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.

A computing device performs initial processing of sensor data. The computing device includes one or more processors and instructions or logic that, when executed by the one or more processors, cause the computing device to perform obtaining sensor data, encoding the sensor data, writing the encoded sensor data to a dynamically allocated buffer, and logging a status of the written encoded sensor data at a static location of the dynamically allocated buffer. The status includes any one or more of memory addresses at which frames of the sensor data begin in the dynamically allocated buffer, valid bit fields corresponding to the frames, and sizes of each of data segments within the frames. The instructions further cause the computing device to perform, in response to receiving a polling request from a computing resource, transmitting the logged status to the computing resource.

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.

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.

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.

A system includes heat generating components in a vehicle and a coolant flow path connected to the heat generating components. The system includes a coolant pump that circulates coolant through the coolant flow path and a reversing mechanism that reverses a direction of circulation of coolant.

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.

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.

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.

Provided herein is a system and method for determining whether a sensor is calibrated and recalibrating of an uncalibrated sensor. The system comprises a sensor system comprising a sensor and an analysis engine configured to determine whether the sensor is uncalibrated. The system further comprises an error handling system configured to determine whether to perform a recalibration in response to the sensor system determining that the sensor is uncalibrated. The error handling system further comprises a recalibration engine configured to perform a recalibration.