Doing More with Less- Cost-Effective Infrastructure for Automotive Vision Capabilities
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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. However, cost-effective deployments of such capabilities have remained elusive. Such deployments will require software infrastructure that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and methods for validating that required processing rates can be maintained.
Currently, advanced driver assistance system (ADAS) capabilities have only been implemented in prototype vehicles using hardware, software, and engineering infrastructure that is very expensive. Prototype hardware commonly includes multiple high-end CPU and GPU chips and expensive LIDAR sensors. Focusing directly on judicious resource allocation, this project seeks to enable more economically viable implementations. Such implementations can reduce system cost by utilizing cameras in combination with low-cost embedded multicore CPU+GPU platforms.
This project focuses on three principal objectives:
- New implementation methods for multiplexing disparate image-processing streams on embedded multicore platforms augmented with GPUs.
- New analysis methods for certifying required stream-processing rates.
- New computer-vision methods for constructing image-processing pipelines.
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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. However, cost-effective deployments of such capabilities have remained elusive. Such deployments will require software infrastructure that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and methods for validating that required processing rates can be maintained.
Currently, advanced driver assistance system (ADAS) capabilities have only been implemented in prototype vehicles using hardware, software, and engineering infrastructure that is very expensive. Prototype hardware commonly includes multiple high-end CPU and GPU chips and expensive LIDAR sensors. Focusing directly on judicious resource allocation, this project seeks to enable more economically viable implementations. Such implementations can reduce system cost by utilizing cameras in combination with low-cost embedded multicore CPU+GPU platforms.
This project focuses on three principal objectives:
- New implementation methods for multiplexing disparate image-processing streams on embedded multicore platforms augmented with GPUs.
- New analysis methods for certifying required stream-processing rates.
- New computer-vision methods for constructing image-processing pipelines.