Gm Global Technolgoy Operations, Inc.

Ramesh Sethu

Prakash Mohan Peranandam

Arun Adiththan

Joseph G D Ambrosio

Manoj G Dixit

Swarup Kumar Mohalik

Srihari Sukumaran

Anil Kumar Sachdev

Shuqing Zeng

Upali Priyantha Mudalige

Shige Wang

Dnyanesh Rajpathak

Soumen De

Anil Bika

Nathan F Thompson

Aravind Gangumalla

Pallab Dasgupta

Prahladavaradan Sampath

Muralikrishnan Kailasam

Prakash M Peranandam

Ambar A Gadkari

Rajeev Ariyattu Chandrasekharan

Devesh B Chokshi

Prasanna Vignesh V Ganesan

Alena Rodionova

Zhiqiang J Zhou

Arun Chakrapani Rao

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.

Charging infrastructure and remote computing system for electric vehicle computing node

Systems and methods for an On-Demand Autonomy (ODA) service. The system includes a selection module of a leader vehicle (Lv) connected to an ODA server to determine whether to confirm a request for an on-demand autonomy (ODA) service which has been broadcast wherein the ODA service request includes control of a follower vehicle (Fv) to a requested location by creating a virtual link between the Lv and the Fv to configure a vehicle platoon to enable transport of the Fv by the Lv wherein the vehicle platoon is a linking of the Lv to the Fv via the virtual link to enable the Lv to assume the control of the Fv to the requested location.

System and method for software architecture for leader vehicle capabilities for an on-demand autonomy (ODA) service

Systems and methods for a cloud-based server system configured for an On-Demand Autonomy (ODA) service are disclosed. The cloud-based server in communication with a first vehicle and a second vehicle to receive a trip request for the ODA service from the first vehicle, and seek an agreement to establish a virtual link with the first vehicle to the second vehicle by identifying one second vehicle by broadcasting the trip request to the group of second vehicles; identifying a second vehicle from submitted responses based on an application using a modeled clone with a set of functionalities to perform a matching operation between the first and second vehicles; and coordinating responses based on results of the matching operation to confirm an acceptance of the agreement; and creating a trip plan for the trip request for the ODA service based on a set of preferences received from each of the vehicles.

System, and methods for implementing a server architecture for an On-Demand Autonomy (ODA) service

A system is provided for updating vehicle software configurations for multiple vehicles. The system includes a network having multiple ECUs carried by the vehicles, with each ECU having an ECU software configuration including software components with one or more versions. The system further includes a network appliance having a processor communicating with the ECUs and a computer readable medium. The processor is programmed to identify coverage points for software components. The processor is further programmed to collect clusters for validating an update of the associated software components and determine optimal sets of clusters, which is less than or equal to a total of the clusters. The processor is further programmed to rank the clusters of the selected optimal set, based on a vehicle coverage percentage of each cluster, and transmit the optimal set for software configuration validation and then finally transmit the validated update to the associated vehicle ECUs.

System and process for vehicle software configuration coverage measurement for update validation

Methods and systems are provided for indicating a driving situation including at least one vehicle. The system includes a first processor, a second processor and an external device. A first processor obtains driving information, encodes the driving information and communicates the encoded driving information to the second processor. The second processor receives and decodes the encoded driving information. The second processor further allocates a predefined indication pattern to the decoded driving information, wherein the predefined indication pattern includes a graphical representation of an upcoming driving event involving the vehicle. An external device visualizes a current driving situation including the vehicle together with the predefined indication pattern.

Methods and systems for indicating a driving situation to external users

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.

Correctness preserving optimization of deep neural networks

A crowdsourced virtual sensor generator is provided which could be generated by a vehicle or provided to the vehicle as, for example, a service. The crowdsourced virtual sensor generator may include, but is not limited to, a communication system configured to receive contributing vehicle sensor data from one or more contributing vehicles, a location of the one or more contributing vehicles and a target vehicle, and a processor, the processor configured to filter the received contributing vehicle sensor data based upon the location of the one or more contributing vehicles and the location of the target vehicle, aggregate the filtered contributing vehicle sensor data into at least one of a data-specific dataset and an application-specific data set, and generate a virtual sensor for the target vehicle, the virtual sensor processing the filtered and aggregated contributing vehicle sensor data to generate output data relative to the location of the target vehicle.

Crowdsource-based virtual sensor generation and virtual sensor application control

A method of automatic identifying linking relationships of requirements in a plurality of requirement documents. Terms in the plurality of requirement documents are identified. A part-of-speech tag is assigned to each term. Each identified term is selected as a focal term. Co-occurring terms within a predetermined distance of the selected focal term are determined. A linking relationship probability is calculated for each co-occurring term associated with the selected focal term. The selected focal terms and associated co-occurring terms between the plurality of requirement documents are compared. A degree of linking relationship is identified between two requirements as a function of a comparison between selected focal terms and the associated co-occurring terms between the plurality of requirement documents. An analysis report identifying the degree of linking relationships between two respective requirements is output.

Automatic linking of requirements using natural language processing

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.

Methodology and tool support for test organization and migration for embedded software

Systems and methods for generating formal software requirements using an informal requirements document having informal requirements and annotations associated with the informal requirements. The systems and methods extract syntax from the annotations and generate artifacts as a function of the syntax.

Systems and methods for generating high-quality formal executable software feature requirements

A method allows for testing software under test (SUT) with respect to a partial design model (PDM) having a boundary which differs from a boundary of the SUT. The method includes recording input information including the SUT, the PDM, and coverage criteria defining a required number of the test cases. Variables in the SUT are identified that correspond to boundary signals for the PDM. Test cases are extracted meeting the coverage criteria. The method may include generating additional test cases at the PDM level and mapping the additional cases with corresponding constraint functions to the boundary of the SUT using a forward/backward propagation and/or heuristics guided technique. A system for testing the SUT includes a host machine and memory. The host machine executes process instructions from memory to identify variables in the SUT that correspond to boundary signals for the PDM, and extracts test cases meeting the coverage criteria.

Software test case generation from a partial design model

A computer-implemented method for evaluating a machine-executable software code specification includes using the computer to generate a system dependence graph corresponding to the software code specification. The system dependence graph includes elements including nodes and edges. The computer evaluates the system dependence graph including selecting a variable modified in the software code specification, providing a control operation node of the system dependence graph corresponding to a control statement in the software code specification with a preferred calibration state, traversing to selected elements of the system dependence graph wherein the selected elements are associated with the selected variable and the preferred calibration state of the control operation node, evaluating only the selected elements of the system dependence graph, and identifying ones of the selected elements whereat a state of the selected variable is modified.

Method and apparatus for analyzing software including a calibrated value

A computer-implemented method for evaluating a machine-executable software code specification includes using the computer to generate a system dependence graph corresponding to the software code specification. The system dependence graph includes elements including nodes and edges, wherein the computer evaluates the system dependence graph. The evaluation of the system dependence graph includes selecting a variable modified in the software code specification, traversing to selected elements of the system dependence graph, the selected elements associated with the selected variable, evaluating only the selected elements of the system dependence graph, and identifying ones of the selected elements whereat a state of the selected variable is modified.

Method and apparatus for analyzing software

A method and tools for providing precise timing analysis scalable to industrial case studies with large numbers of tasks and messages are provided, including the capability to model and analyze task and message response times; ECU usage; bus usage; end-to-end latency of task/message chains; and timing synchronization problems in task/message graphs. System tasks and messages are modeled in a formalism known as calendar automaton. Models are written in a modeling language such as Promela and instrumented with code specific to the analysis specification. Models and instrumentation are automatically generated from the system description and analysis specification. The system model is subjected to exhaustive state space exploration by a compatible model checker, such as SPIN. During exploration, the instrumented code produces results for different timing analyses. Optimization techniques are provided to generate models which require less memory and time for analysis and make the method scalable to large, industrial case studies.

Timing analysis using formal methods

A system and method for automatic formal verification of an executable model includes an assertion monitor configured to verify a system against an assertion in a specification. The assertion monitor includes a parser configured to generate a propositional formula representing the assertion in the specification using Boolean propositions, a filter configured to generate a run of the system using truth assignments for the propositional symbols, and a trace verifier configured to verify the assertion using the run of the system using truth assignments for the propositional symbols and the propositional formula.

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Bengaluru, India

Ramesh Sethu