Abstract:
A method for using idle computing power of an electric vehicle includes sending the computing tasks and the task rates for performing the computing tasks to a vehicle controller of the electric vehicle and receiving an acceptance signal from the vehicle controller. The acceptance signal is indicative that the vehicle controller accepted to perform the computing tasks. The method further includes commanding a charging infrastructure to supply electrical power the electric vehicle while the vehicle controller performs the computing tasks.
Abstract:
Methods and systems for an On-Demand Autonomy (ODA) system are provided. A method includes: receiving a request for ODA service from the Fv, wherein the request includes a location of the Fv; when the Lv is within a first distance of the location of the Fv: identifying the Fv within a scene of an environment of the Lv; identifying an orientation of the Fv within the scene of the environment of the Lv; and determining a second location for the Lv to begin the ODA service; when the Lv is within a second distance of the second location, determining a closeness of other vehicles within a second scene of the environment of the Lv; confirming the orientation of the Fv in the second scene; performing a handshake method with the Fv to create a virtual link between the Lv and the Fv; and performing at least one of pulling and parking platooning methods using the created virtual link.
Abstract:
A method for reducing the number of neurons in a trained deep neural network (DNN) includes classifying layer types in a plurality of hidden layers; evaluating the accuracy of the DNN using a validation set of data; and generating a layer specific ranking of neurons, wherein the generating includes: analyzing, using the validation set of data for one or more of the plurality of hidden layers, the activation function for each neuron in the analyzed layers to determine an activation score for each neuron; and ranking, on a layer type basis, each neuron in the analyzed layers based on the neuron's activation score to generate a layer specific ranking of neurons. The method further includes removing a number of lower ranked neurons from the DNN that does not result in the DNN after the removal of selected lower ranked neurons to fall outside of an accuracy threshold limit.
Abstract:
A system for use in analyzing software code using a symbolic-execution technique. The system includes a hardware-based processing unit, and a non-transitory computer-readable storage component including a (i) concurrency-analysis module, (ii) a lightweight-analysis module, and (iii) a heavyweight-analysis module. The concurrency-analysis module, when executed by the hardware-based processing unit receives initial code and generates a potential interference matrix using the initial code. The lightweight-analysis module, when executed by the hardware-based processing unit, generates a final interface matrix using the potential interference matrix. The heavyweight-analysis module, when executed by the hardware-based processing unit, generates one or more test cases using the potential interference matrix. Various aspects of the present technology includes a non-transitory computer-readable storage devices configured to perform any of the operations described, algorithms to perform any of the operations described, and the methods or processes including the operations performed by these systems, storage devices, and algorithms.
Abstract:
A method of establishing traceability for embedded software systems. A design code database is provided for an embedded software system. A test suite database including a plurality of test cases is structured for testing design code of the embedded software system. The structuring of the test cases provides a correspondence from a respective test case to a respective portion of the design code. A processor receives a design code modification to the embedded software. An associated test case is identified for testing the modified design code being based on traceability data. The associated test case is revised to accommodate the modified design code. The modified test cases are integrated into the test suite. A traceability database establishes a one-to-one correspondence between the modified design coder and the modified test case is updated.