Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path—without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: A method and system for operating an enterprise via an optimized business model. An output benchmark value is generated. An initial benchmark value for a resource of an enterprise is updated based on the output benchmark value. The business model is updated. A process is iteratively performed based on the updated benchmark value and model, until the benchmark value is changed by less than a predetermined threshold to generate the optimized business model. The enterprise is operated in accordance with the optimized model which includes: generating performance measures of usage of a computer resource, dynamically displaying a dashboard of the performance measures, determining from the displayed performance measures that the computer resource is a current bottleneck or is likely to become a bottleneck in the near future, and optimizing the computer resource's usage using the displayed performance measures to reduce data throughput delay and increase throughput of bottleneck operations.

Abstract: A method and system for operating an enterprise via an optimized business model. An output benchmark value is generated. An initial benchmark value for a resource of an enterprise is updated based on the output benchmark value. The business model is updated. A process is iteratively performed based on the updated benchmark value and model, until the benchmark value is changed by less than a predetermined threshold to generate the optimized business model. The enterprise is operated in accordance with the optimized model which includes: generating performance measures of usage of a computer resource, dynamically displaying a dashboard of the performance measures, determining from the displayed performance measures that the computer resource is a current bottleneck or is likely to become a bottleneck in the near future, and optimizing the computer resource's usage using the displayed performance measures to reduce data throughput delay and increase throughput of bottleneck operations.

Abstract: A method and system for operating an enterprise in accordance with an optimized enterprise level business model. A model optimization engine is iteratively processed based on an updated benchmark value and model, until updating the benchmark value involves changing the benchmark value by less than a predetermined benchmark value error threshold to generate the optimized enterprise-level business model. The enterprise is operated in accordance with the optimized enterprise level business model, including: generating and dynamically displaying real-time performance measures of usage of a computer resource during execution of a business process, determining from the performance measures that the computer resource is a bottleneck, and the computer resource's usage is optimized using the performance measures to reduce data throughput delay and increase throughput of bottleneck operations during execution of the business process.

Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: A method and system for operating an enterprise in accordance with an optimized enterprise level business model. A model optimization engine is iteratively processed based on an updated benchmark value and model, until updating the benchmark value involves changing the benchmark value by less than a predetermined benchmark value error threshold to generate the optimized enterprise-level business model. The enterprise is operated in accordance with the optimized enterprise level business model, including: generating and dynamically displaying real-time performance measures of usage of a computer resource during execution of a business process, determining from the performance measures that the computer resource is a bottleneck, and the computer resource's usage is optimized using the performance measures to reduce data throughput delay and increase throughput of bottleneck operations during execution of the business process.

Abstract: A method and system for operating an enterprise in accordance with an optimized enterprise level business model. A model optimization engine is iteratively processed based on an updated benchmark value and model, until updating the benchmark value involves changing the benchmark value by less than a predetermined benchmark value error threshold to generate the optimized enterprise-level business model. The enterprise is operated in accordance with the optimized enterprise-level business model, including: generating and dynamically displaying real-time performance measures of usage of a computer resource during execution of a business process, determining from the performance measures that the computer resource is a bottleneck, and the computer resource's usage is optimized using the performance measures to reduce data throughput delay and increase throughput of bottleneck operations during execution of the business process.

Abstract: A method and system for operating an enterprise in accordance with an optimized enterprise level business model. A model optimization engine is iteratively processed based on an updated benchmark value and model, until updating the benchmark value involves changing the benchmark value by less than a predetermined benchmark value error threshold to generate the optimized enterprise-level business model. The enterprise is operated in accordance with the optimized enterprise-level business model, including: generating and dynamically displaying real-time performance measures of usage of a computer resource during execution of a business process, determining from the performance measures that the computer resource is a bottleneck, and the computer resource's usage is optimized using the performance measures to reduce data throughput delay and increase throughput of bottleneck operations during execution of the business process.

Abstract: The present disclosure relates to methods and systems for developing, implementing, transforming, and governing a business model of an enterprise. In some embodiments, a method of implementing and managing a business model of an enterprise includes defining a business model using a business model tool that is executed by a computer, wherein the business model is based on interrelated business strategy, business goal and business constraint data, and includes a business service, inputting into the computer a first set of data representing a benchmark performance value of the business service, and operating the enterprise in accordance with the business model. The enterprise is monitored to determine an actual performance value of the business service, and the actual performance value and the benchmark performance value are processed in the computer to affect a comparison therebetween. The business model is modified based on the comparison.

Abstract: A method, system and computer program product for accurately predicting capacity requirements for Information Technology (IT) resources in physical, virtual, cloud and hybrid cloud environments. The business demand is translated into IT application demand, which is translated into IT architecture layer demand. Resource groups are created for each IT application, IT architecture layer and resource type combination. The utilization for each IT resource group within the IT architecture layers is computed using the created resource groups, IT application demand and IT architecture layer demand. Under or over-utilized IT resource groups may be identified using the computed utilization for each IT resource group. Upon identifying such IT resources, an optimal resource profile (e.g., number of servers) for each resource group is computed.

Abstract: A method, system and computer program product for selecting the optimal cloud service provider(s) to service a user's needs. A physical capacity of servers in a non-virtualized data center is converted into a cloud capacity to be used. A list of cloud service providers may be generated from a catalog of providers based on the cloud capacity to be used. Additional requirements and constraints received from the user are used to select an optimal cloud service provider(s) from the generated list of cloud service providers.

Abstract: A method, system and computer program product for accurately predicting capacity requirements for Information Technology (IT) resources in physical, virtual, cloud and hybrid cloud environments. The business demand is translated into IT application demand, which is translated into IT architecture layer demand. Resource groups are created for each IT application, IT architecture layer and resource type combination. The utilization for each IT resource group within the IT architecture layers is computed using the created resource groups, IT application demand and IT architecture layer demand. Under or over-utilized IT resource groups may be identified using the computed utilization for each IT resource group. Upon identifying such IT resources, an optimal resource profile (e.g., number of servers) for each resource group is computed.

Abstract: The present disclosure relates to methods and systems for developing, implementing, transforming, and governing a business model of an enterprise. In some embodiments, a method of implementing and managing a business model of an enterprise includes defining a business model using a business model tool that is executed by a computer, wherein the business model is based on interrelated business strategy, business goal and business constraint data, and includes a business service, inputting into the computer a first set of data representing a benchmark performance value of the business service, and operating the enterprise in accordance with the business model. The enterprise is monitored to determine an actual performance value of the business service, and the actual performance value and the benchmark performance value are processed in the computer to affect a comparison therebetween. The business model is modified based on the comparison.