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: Methods and apparatus are provided for selecting a computational platform. The method includes collecting platform feature data for each of a plurality of existing computational platforms in a particular class of computational platforms. The platform feature data for each of the plurality of existing computational platforms includes a plurality of design parameters and a performance measurement for the computational platform to complete a benchmarking software component. The method further includes training a mathematical model using the collected platform feature data to devise a predictive model for predicting a predicted performance measurement for a computational platform in the particular class of computational platforms when provided with design parameters, predicting, with the predictive model, the predicted performance measurement of a new computational platform that is not available for testing, and selecting a computational platform using the predicted performance measurement.

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 herein are intrusion detection systems and algorithms for networked vehicle controllers and devices, methods for making/using such systems and algorithms, and motor vehicles with a network of ECUs and network-profiling intrusion detection capabilities. A method for detecting intrusions into an onboard network of vehicle controllers includes determining the current state of operation of a vehicle, and identifying a network traffic pattern table corresponding to the vehicle's current state of operation. Network traffic flow for one of the in-vehicle controllers is monitored when exchanging data over the Ethernet communication interface while the motor vehicle is operating in the current state of operation. The method then determines if a traffic characteristic of the monitored network traffic flow is outside a calibrated boundary that is determined from the network traffic pattern table.

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 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.

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: Examples of techniques for dynamically selecting a batch size used in vehicle camera image processing are disclosed. In one example implementation, a method includes generating, by a processing device, a batch table and a mode table. The method further includes determining, by the processing device, image processing performance requirements for a current mode of a vehicle using the mode table, the vehicle comprising a plurality of cameras configured to capture a plurality of images. The method further includes selecting, by the processing device, a batch size and a processing frequency based at least in part on the image processing performance requirements for the current mode of the vehicle. The method further includes processing, by an accelerator, at least a subset of the plurality of images based at least in part on the batch size and processing frequency.

Abstract: A system and method for object detection and classification includes receiving by a controller a first data of a scene, wherein the data reflects the scene at a first time, and performing a first classification of at least one object within the scene based on the data. The method includes determining a projected location of the at least one object corresponding to an estimated location at a second time. The method includes receiving a second data of the scene reflecting the scene at the second time. The method includes determining whether the projected location of the object corresponds to the location of the object as reflected by the second data. The method includes determining whether performing a second classification of the object is necessary based on the determination of whether the projected location corresponds to the location of the object reflected by the second data.

Abstract: A system and method for transmitting a plurality of signals. The system includes a transmitting terminal configured to obtain and pack the plurality of signals in multiple signal sequences, with each of the plurality of signals having respective predefined periods. A receiving terminal is configured to receive and unpack the multiple signal sequences from the transmitting terminal. A controller is in communication with the transmitting terminal and the receiving terminal and configured to store a look-up table. The look-up table includes a respective pre-identifier and respective identifier for each of the plurality of signals. The controller is configured to determine if the respective predefined periods vary in magnitude within a frame. If so, the controller is configured to obtain the respective pre-identifiers and respective identifiers for each of the plurality of signals. The multiple signal sequences are based at least partially on the respective pre-identifiers and the respective identifiers.

Abstract: A system and method for object detection and classification includes receiving by a controller a first data of a scene, wherein the data reflects the scene at a first time, and performing a first classification of at least one object within the scene based on the data. The method includes determining a projected location of the at least one object corresponding to an estimated location at a second time. The method includes receiving a second data of the scene reflecting the scene at the second time. The method includes determining whether the projected location of the object corresponds to the location of the object as reflected by the second data. The method includes determining whether performing a second classification of the object is necessary based on the determination of whether the projected location corresponds to the location of the object reflected by the second data.

Abstract: Examples of techniques for dynamically selecting a batch size used in vehicle camera image processing are disclosed. In one example implementation, a method includes generating, by a processing device, a batch table and a mode table. The method further includes determining, by the processing device, image processing performance requirements for a current mode of a vehicle using the mode table, the vehicle comprising a plurality of cameras configured to capture a plurality of images. The method further includes selecting, by the processing device, a batch size and a processing frequency based at least in part on the image processing performance requirements for the current mode of the vehicle. The method further includes processing, by an accelerator, at least a subset of the plurality of images based at least in part on the batch size and processing frequency.

Abstract: Methods and systems are provided for controlling a temperature of a processor of a controller. In one embodiment, a method includes: identifying a status of at least one task of a plurality of software tasks performed on a first processor to be a hot task based on the software task's contribution to a temperature of the first processor; and selectively controlling the temperature of the first processor based on the identified status.

Abstract: A system and method for transmitting a plurality of signals. The system includes a transmitting terminal configured to obtain and pack the plurality of signals in multiple signal sequences, with each of the plurality of signals having respective predefined periods. A receiving terminal is configured to receive and unpack the multiple signal sequences from the transmitting terminal. A controller is in communication with the transmitting terminal and the receiving terminal and configured to store a look-up table. The look-up table includes a respective pre-identifier and respective identifier for each of the plurality of signals. The controller is configured to determine if the respective predefined periods vary in magnitude within a frame. If so, the controller is configured to obtain the respective pre-identifiers and respective identifiers for each of the plurality of signals. The multiple signal sequences are based at least partially on the respective pre-identifiers and the respective identifiers.

Abstract: Methods and systems are provided for controlling a temperature of a processor of a controller. In one embodiment, a method includes: identifying a status of at least one task of a plurality of software tasks performed on a first processor to be a hot task based on the software task's contribution to a temperature of the first processor; and selectively controlling the temperature of the first processor based on the identified status.

Abstract: Methods and apparatus are provided for selecting a computational platform. The method includes collecting platform feature data for each of a plurality of existing computational platforms in a particular class of computational platforms. The platform feature data for each of the plurality of existing computational platforms includes a plurality of design parameters and a performance measurement for the computational platform to complete a benchmarking software component. The method further includes training a mathematical model using the collected platform feature data to devise a predictive model for predicting a predicted performance measurement for a computational platform in the particular class of computational platforms when provided with design parameters, predicting, with the predictive model, the predicted performance measurement of a new computational platform that is not available for testing, and selecting a computational platform using the predicted performance measurement.