Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receive, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, compare the output generated by the first neural network with the output generated by the second neural network, and generate an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold.

Abstract: A method for facilitating uses of a code for vehicle experiences includes receiving code data from the code in a sign. The sign is disposed outside a host vehicle. The method further includes establishing a connection with a service using the code data obtained from the code and in response to establishing the connection with the service, receiving service data. The service data includes information about the service. The method further includes, in response to receiving the service data, controlling the host vehicle according to the service data received after establishing the connection with the service.

Abstract: A perception processing system includes a memory and a main controller. The main controller includes modules and implements a data processing pipeline including algorithm stages, which are executed in parallel relative to sets of data and are executed sequentially relative to each of the sets of data. The algorithm stages share resources of the modules and the memory to process the sets of data and generate perception information. One of the modules executes global and local controllers. The global controller sets a processing rate for the local controllers. The local controllers monitor current processing rates of the algorithm stages. When one of the current processing rates is less than the set processing rate, the corresponding one of the local controllers sends a first signal to the global controller and in response the global controller sends a broadcast signal to the local controllers to adjust the current processing rates.

Abstract: A system for an attention-based perception includes a camera device configured to provide an image of an operating environment of a vehicle. The system further includes a computerized device monitoring the image, analyzing sensor data to identify a feature in the image as corresponding to an object in the operating environment and assign a score for the feature based upon an identification, a location, or a behavior of the object. The computerized device is further operable to define candidate regions of interest upon the image, correlate the score for the feature to the candidate regions of interest to accrue a total region score, select some of the candidate regions for analysis based upon the total region scores, and analyze the portion of the candidate regions to generate a path of travel output. The system further includes a device controlling the vehicle based upon the output.

Abstract: A controller area network fault detection and recovery system and method may include a fault detection module, a fault remediation module, a checkpoint manager, and a recovery manager configured to select one or more of the fault remediation mechanisms based upon detected CAN faults. Remediation of detected CAN faults is controlled at a CAN driver software level in accordance with selected fault remediation mechanisms in a predetermined ordered hierarchy.

Abstract: A vehicle communication and control system includes a servicing host capable of exchanging data with a vehicle. The servicing host provides a vehicle service and includes a service identifier (ID) that indicates the vehicle service. The vehicle is configured to actively detect the service ID and to determine the vehicle service in response to detecting the service ID. The vehicle and the servicing host establish a wireless connection to exchange data and automatically initiate the vehicle service in response to detecting the service ID.

Abstract: A vehicle communication and control system includes a servicing host capable of exchanging data with a vehicle. The servicing host provides a vehicle service and includes a service identifier (ID) that indicates the vehicle service. The vehicle is configured to actively detect the service ID and to determine the vehicle service in response to detecting the service ID. The vehicle and the servicing host establish a wireless connection to exchange data and automatically initiate the vehicle service in response to detecting the service ID.

Abstract: A system includes a queue, a memory and a controller. The queue is configured to transfer a message between a first thread and a second thread, where the first thread and the second thread are implemented as part of a single process, and where an amount of data corresponding to the message is less than a set amount of data. The memory is configured for sharing data between the first thread and the second thread, wherein an amount of the data shared between the first thread and the second thread is greater than the set amount of data. The controller is configured to execute the single process including concurrently executing (i) a first middleware node process as the first thread, and (ii) a second middleware node process as the second thread.

Abstract: A signal processing system includes a central processing unit (CPU) in communication with an accelerator, and an instruction scheduler in communication with the accelerator. A first memory device including a first instruction set is configured to operate the accelerator, a second instruction set is configured to operate the CPU, and a second memory device is configured to receive a datafile. The accelerator includes a plurality of processing engines (PEs) and an instruction scheduler, the instruction set includes a plurality of operators, and the instruction scheduler is configured to implement the operators in the accelerator employing the PEs. The CPU employs the operators implemented in the accelerator to analyze the datafile to extract a feature therefrom.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes capturing a plurality of images at a camera associated with a vehicle and storing image data associated with the plurality of images to a memory. The method further includes dispatching vehicle perception tasks to a plurality of processing elements of an accelerator in communication with the memory. The method further includes performing, by at least one of the plurality of processing elements, the vehicle perception tasks for the vehicle perception using a neural network, wherein performing the vehicle perception tasks comprises performing an activation bypass for values below a first threshold, and performing weight pruning of synapses and neurons of the neural network based at least in part on a second threshold. The method further includes controlling the vehicle based at least in part on a result of performing the vehicle perception tasks.

Abstract: A method in a multiprocessor system for processing multiple perception streams is disclosed. The method comprises: reading data from a plurality of perception streams according to a reading schedule determined by a predetermined policy, each perception stream comprising perception data from a different perception sensor; assigning a unique identification tag to each perception stream; writing each perception stream with its unique identification tag to a server input queue based on the predetermined policy; and processing the tagged perception streams using a server. The processing includes: retrieving tagged perception streams from the server input queue; applying a processing algorithm to process the retrieved tagged perception streams; and outputting the processed perception streams to a server output queue.

Abstract: A controller area network fault detection and recovery system and method may include a fault detection module, a fault remediation module, a checkpoint manager, and a recovery manager configured to select one or more of the fault remediation mechanisms based upon detected CAN faults. Remediation of detected CAN faults is controlled at a CAN driver software level in accordance with selected fault remediation mechanisms in a predetermined ordered hierarchy.

Abstract: A method for controlling a vehicle includes: receiving, by a controller, route data, wherein the route data is continuously updated while the vehicle is moving, and the vehicle includes a plurality of vehicle operating modes; receiving, by the controller, feature data, wherein the feature data is information about a plurality of features needed for each of the plurality of vehicle operating modes; determining, by the controller, a plurality of ranges for each of the plurality of vehicle operating modes, wherein each of the plurality of ranges is a function of the route data and the feature data for each of the plurality of vehicle operating modes; and commanding, by the controller, a user interface to display a list of range-mode combinations, wherein the list of range-mode combinations includes the plurality of ranges for each of the plurality of vehicle operating modes.

Abstract: Presented are embedded control systems with logic for computation and data sharing, methods for making/using such systems, and vehicles with distributed sensors and embedded processing hardware for provisioning automated driving functionality. A method for operating embedded controllers connected with distributed sensors includes receiving a first data stream from a first sensor via a first embedded controller, and storing the first data stream with a first timestamp and data lifespan via a shared data buffer in a memory device. A second data stream is received from a second sensor via a second embedded controller. A timing impact of the second data stream is calculated based on the corresponding timestamp and data lifespan. Upon determining that the timing impact does not violate a timing constraint, the first data stream is purged from memory and the second data stream is stored with a second timestamp and data lifespan in the memory device.

Abstract: A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.

Abstract: A method in a multiprocessor system for processing multiple perception streams is disclosed. The method comprises: reading data from a plurality of perception streams according to a reading schedule determined by a predetermined policy, each perception stream comprising perception data from a different perception sensor; assigning a unique identification tag to each perception stream; writing each perception stream with its unique identification tag to a server input queue based on the predetermined policy; and processing the tagged perception streams using a server. The processing includes: retrieving tagged perception streams from the server input queue; applying a processing algorithm to process the retrieved tagged perception streams; and outputting the processed perception streams to a server output queue.

Abstract: Examples of techniques for using fixed-point quantization in deep neural networks are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes capturing a plurality of images at a camera associated with a vehicle and storing image data associated with the plurality of images to a memory. The method further includes dispatching vehicle perception tasks to a plurality of processing elements of an accelerator in communication with the memory. The method further includes performing, by at least one of the plurality of processing elements, the vehicle perception tasks for the vehicle perception using a neural network, wherein performing the vehicle perception tasks comprises quantizing a fixed-point value based on an activation input and a synapse weight. The method further includes controlling the vehicle based at least in part on a result of performing the vehicle perception tasks.

Abstract: Described herein are systems, methods, and computer-readable media for generating and training a high precision low bit convolutional neural network (CNN). A filter of each convolutional layer of the CNN is approximated using one or more binary filters and a real-valued activation function is approximated using a linear combination of binary activations. More specifically, a non-1×1 filter (e.g., a k×k filter, where k>1) is approximated using a scaled binary filter and a 1×1 filter is approximated using a linear combination of binary filters. Thus, a different strategy is employed for approximating different weights (e.g., 1×1 filter vs. a non-1×1 filter). In this manner, convolutions performed in convolutional layer(s) of the high precision low bit CNN become binary convolutions that yield a lower computational cost while still maintaining a high performance (e.g., a high accuracy).

Abstract: A stream manager for managing the distribution of instructions to a plurality of processing devices includes a dispatcher module configured to: receive multiple instruction streams, wherein each instruction stream includes a plurality of requested computations for processing perception data from a perception data source; partition each instruction stream into a plurality of partitions based on type of device to perform a requested computation from the instruction stream; assign a release time and deadline to each partition, and dispatch partition computations to a plurality of scheduling queues to distribute processing of the partition computations amongst the plurality of processing devices. The plurality of scheduling queues include: a plurality of CPU schedulers, wherein each CPU scheduler is assigned to a specific CPU and a specific scheduling queue; and a plurality of accelerator schedulers, wherein each accelerator scheduler is assigned to a specific scheduling queue and a specific type of accelerator.

Abstract: A stream manager for managing the distribution of instructions to a plurality of processing devices includes a dispatcher module configured to: receive multiple instruction streams, wherein each instruction stream includes a plurality of requested computations for processing perception data from a perception data source; partition each instruction stream into a plurality of partitions based on type of device to perform a requested computation from the instruction stream; assign a release time and deadline to each partition, and dispatch partition computations to a plurality of scheduling queues to distribute processing of the partition computations amongst the plurality of processing devices. The plurality of scheduling queues include: a plurality of CPU schedulers, wherein each CPU scheduler is assigned to a specific CPU and a specific scheduling queue; and a plurality of accelerator schedulers, wherein each accelerator scheduler is assigned to a specific scheduling queue and a specific type of accelerator.