Abstract: A multiprocessor unit (MPU) in an autonomous driving vehicle (ADV) can provide hard real-time performance. In an embodiment, the MPU can include a hypervisor used to virtualize multiple cores of the MPU, which can further be partitioned into two sets of cores that are isolated from each other. The first set of cores are designated to run real-time related services as trusted applications directly on the hypervisor, and the real-time related services are given higher priority than kernel-level threads on the first set of cores. The second set of cores are designated to run a kernel of an operating system (e.g., Linux). Further, the kernel is patched using a hard real-time open source package to achieve hard real-time performance. An open source package can be used for interprocess communication (IPC) between different electronic control units (ECU) in the ADV.

Abstract: In one embodiment, a vehicle operating system (VOS) that can be partially ported to different types of microcontroller units (MCUs) includes at least one multiprocessor unit (MPU) with an operating system kernel running thereon, and at least one microcontroller unit (MCU) with multiple cores. Each core includes a set of unified application programming interfaces (APIs) for loading one or more MCU drivers corresponding to a type of the MCU, and one or more I/O drivers corresponding to a type of each of the one or more I/O devices associated with the MCU. The set of unified APIs includes at least one API for each service, and can vertically integrate a device path for the service from a hardware layer of the core to the service layer of the core. The VOS further includes multiple pairs of hardware-protected memories associated with each core to enable interprocess communication between the cores.

Abstract: The present application relates to a distributed antenna system, a method and an apparatus. The distributed antenna system comprises a digital-analog expansion unit and a remote cascade chain, the remote cascade chain comprising multiple remote units cascadingly connected by means of radio frequency cable, and a first remote unit of the remote cascade chain being connected to the digital-analog expansion unit by means of radio frequency cable. The digital-analog expansion unit is used to perform a baseband processing operation on a received external signal, and to perform interconversion of an analog RF signal and a digital RF signal.

Abstract: In an embodiment, a method verifies functionality of computation hardware on a device under test (DUT). The method loads a software test program onto the DUT, wherein the DUT includes computation hardware components. The method executes the software test program on the DUT to test the hardware components. During the testing, the software test program instructs one or more devices external to the DUT to provide one or more signals to one or more of the computation hardware components. The software test program generates a set of test results in response to testing the computation hardware components based on the one or more signals.

Abstract: In an embodiment, an autonomous driving vehicle (ADV) determines a LIDAR type, from of a plurality of LIDAR types, of a LIDAR unit. Responsive to determining the LIDAR type of the LIDAR unit, the ADV configures an adaptive LIDAR data processing system based on the LIDAR type. The adaptive LIDAR data processing system supports each one of the plurality of LIDAR types. In turn, responsive to configuring the adaptive LIDAR data processing system, the ADV establishes communication between the LIDAR unit and a host system using the adaptive LIDAR data processing system.

Abstract: In some implementations, a method is provided. The method includes determining a first set of data acquisition characteristics of a first sensor of an autonomous driving vehicle. The method also includes determining a second set of data acquisition characteristics of a second sensor of the autonomous driving vehicle. The method further includes synchronizing a first data acquisition time of the first sensor and a second data acquisition time of the second sensor, based on the first set of data acquisition characteristics and the second set of data acquisition characteristics. The first sensor obtains first sensor data at the first data acquisition time. The second sensor obtains second sensor data at the second data acquisition time.

Abstract: In one embodiment, an exemplary computer-implemented method of storing point cloud data in an autonomous driving vehicle can include the operations of receiving raw point cloud data from a LiDAR sensor mounted on the autonomous driving vehicle, the raw point cloud data representing cloud data points acquired in response to laser beams emitted at a given angle; retrieving configuration information of the LiDAR sensor, the configuration information including at least a number of laser lines of the LiDAR sensor. The method further includes the operations of constructing, based on the configuration information, a data structure that includes a data entry for each of the cloud data points, the data entry including multiple fields for storing attributes of the cloud data point, each field having a bit width determined based on the configuration information using a predetermined algorithm; and writing the cloud data points to a storage medium using the data structure.

Abstract: Embodiments relate to systems and methods to optimize quantization of tensors of an AI model. According to one embodiment, a system receives an AI model having one or more layers. The system receives a number of input data for offline inferencing and applies offline inferencing to the AI model based on the input data to generate offline data distributions for the AI model. The system quantizes one or more tensors of the AI model based on the offline data distributions to generate a low-bit representation AI model, where each layer of the AI model includes the one or more tensors, where the one or more tensors include the one or more tensors. In one embodiment, the system applies online inferencing using the low-bit representation AI model to generate online data distributions for a feature map, and quantizes a feature map tensor based on the online data distributions.

Abstract: A method and system is disclosed for protecting neural network models by segmenting partitions of the models into segments of pre-configured memory size, hashing the segmented models, and concatenating the hash segments. The concatenated hash segment may be further hashed, encrypted, and stored with the neural network models as an executable loadable file (ELF) in memories external to the neural network prior to the use of the models by the neural network. The models may include model weights of the inference layers and metadata. The model weights and the metadata may be hashed as separate hash segments and concatenated. Segmenting the models into segments of pre-configured memory size and hashing the segmented models offline prior to the operation of the neural network enables rapid validation of the models when the models are used in the inference layers during online operation of the neural network.

Abstract: In some implementations, a method of verifying operation of a sensor is provided. The method includes causing a sensor to obtain sensor data at a first time, wherein the sensor obtains the sensor data by emitting waves towards a detector. The method also includes determining that the detector has detected the waves at a second time. The method further includes receiving the sensor data from the sensor at a third time. The method further includes verifying operation of the sensor based on at least one of the first time, the second time, or the third time.

Abstract: A data processing system includes a host system and one or more expansion devices coupled to the host system over a bus. The host system may include one or more processors and a memory storing instructions, which when executed, cause the processors to perform autonomous driving operations to drive an autonomous driving vehicle (ADV). Each expansion device includes a switch device and one or more processing modules coupled to the switch device. Each processing module can be configured to perform at least one of the autonomous driving operations offloaded from the host system. At least one of the processing modules can be configured as a client node to perform an action in response to an instruction received from the host system. Alternatively, it can be configured as a host node to distribute a task to another client node within the expansion device. This host node in the expansion device can further cooperate with the host system via a host-to-host connection.

Abstract: An ADV includes a method to combine data from multiple sensors. The method compresses video data from a camera to generate compressed video data. The compressed video data are segmented. The method time synchronizes each segment of the compressed video data with data from other sensors. The method then combines each segment of the compressed video data with the corresponding time-synchronized sensor data for the other sensors. In one embodiment, each segment of the compressed video data is independently decodable. In another embodiment, each segment of the compressed video data includes a compressed video unit that is prepended with a buffered portion of the compressed video data that immediately precede the compressed video unit. The length of the compressed video unit is smaller than the length of the independently decodable segment to offer finer granularity in time synchronizing the compressed video data with the other sensor data with a tradeoff.

Abstract: A method, apparatus, and system for timing synchronization between multiple computing nodes in an autonomous vehicle host system is disclosed. Timing of a first computing node of an autonomous vehicle host system is calibrated based on an external time source. A first timing message is transmitted from the first computing node to a second computing node of the autonomous vehicle host system via a first communication channel between the first computing node and the second computing node. Timing of the second computing node is calibrated based on the first timing message, wherein immediately subsequent to the calibration of timing of the second computing node, timing of the first computing node and of the second computing node is synchronized.

Abstract: In one embodiment, a computer-implemented method of performing a secure boot operation in an autonomous driving vehicle includes reading a first marker from a storage device in which the storage device includes a plurality of partitions and at least the first marker. The plurality of partitions includes a first partition including stored software, the first marker associated with the first partition, and wherein the first marker includes a unique identifier and an authentication code. The method further includes determining if the read first marker associated with the first partition is valid during a boot-up operation and executing the stored software in the first partition if the read first marker is determined valid.

Abstract: Systems and methods are disclosed for performing manufacturing testing on an autonomous driving vehicle (ADV) sensor board. A sensor unit of the ADV includes a plurality of sensor I/O channels that provide information to the ADV perception and planning module, to navigate the ADV. An array of sensors is emulated on a sensor unit test board. The sensor unit includes a small software that manages the flow of testing the sensor unit. The sensor unit test board provides emulated sensor data for, e.g., GPS, LIDAR, RADAR, inertial measurement, one or more cameras, humidity, temperature, and pressure, and throttle, braking, and steering inputs. Each emulated sensor includes its own data validity checker to ensure that each sensor I/O channel of the sensor unit is tested. The sensor unit test board can include an LED for each I/O channel that indicates a pass/fail status of the test for the I/O channel.

Abstract: In one embodiment, a sensor unit to be utilized in an autonomous driving vehicle (ADV) includes a sensor interface that can be coupled to a number of sensors mounted on a number of different locations of the ADV. The sensor unit further includes a host interface that can be coupled to a host system such as a planning and control system utilized to autonomously drive the vehicle. The sensor unit further includes a number of data transfer modules corresponding to the sensors. Each of the data transfer modules can be configured to operate in one of the operating modes, dependent upon the type of the corresponding sensor. The operating modes include a low latency mode, a high bandwidth mode, and a memory mode.

Abstract: A method to perform video compression for ADV is disclosed. The method receives multiple frames of image data from multiple cameras. Metadata are appended to each frame of the image data to generate one of multiple frames of uncompressed image data as the image data are received. The frames of uncompressed image data may be stored. To compress the image data later, the method retrieves the frames of uncompressed image data, extracts the metadata from each frame of the uncompressed image data to generate one of multiple frames of processed image data. The method compresses each frame of the processed image data with the metadata extracted to generate one of multiple frames of compressed image data. The method reattaches the metadata to a corresponding frame of the compressed image data to generate one of multiple compressed image frames. The metadata supports time synchronization and error handling of the image data.

Abstract: The disclosure describes various embodiments of validating data synchronization between an active sensor and a passive sensor. According to an exemplary method of validating sensor synchronization between an active sensor and a passive sensor, a synchronization device receives a first signal from the active sensor, the first signal indicating that the active sensor has transmitted laser points to a measure board. In response to the first signal, the synchronization device transmits a second signal to the passive sensor to trigger the passive sensor to capture an image of the measure board. A synchronization validation application can perform an analysis of the image of the measure board in view of timing of the first signal and second signal to determine whether the passive sensor and the active sensor are synchronized with each other.

Abstract: Diagnosing a sensor processing unit of an autonomous driving vehicle is described. An example computer-implemented method can include transmitting an executable image of a sensor processing application from a host system to the sensor processing unit via at least one of a universal asynchronous receiver-transmitter (UART) or an Ethernet connection. The method also includes causing the sensor processing unit to execute and launch the executable image of the sensor processing application in the DRAM from the eMMC storage device. The method also includes transmitting a sequence of predetermined commands to the executed sensor processing application to perform a plurality of sensor data processing operations on sensor data obtained from a plurality of sensors or sensor simulators associated with an autonomous driving vehicle.

Abstract: In some implementations, a method of verifying operation of a sensor is provided. The method includes causing a sensor to obtain sensor data at a first time, wherein the sensor obtains the sensor data by emitting waves towards a detector. The method also includes determining that the detector has detected the waves at a second time. The method further includes receiving the sensor data from the sensor at a third time. The method further includes verifying operation of the sensor based on at least one of the first time, the second time, or the third time.