Compressed Feature Localization for Autonomous Vehicles
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Solution Overview
Problem
Current technologies face challenges in accurately localizing objects within dynamic environments using compressed feature representations, particularly for autonomous vehicles, as they require efficient data processing and precise object positioning to ensure safety and efficient navigation.
Innovation Solution
A computer-implemented method utilizing machine-learned feature extraction models to generate compressed feature representations of environments, allowing for the determination of an object's localized state by comparing source and target feature representations, and adjusting model parameters based on loss evaluation to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compressed feature representations are used for localization, then computational efficiency and resource utilization improve, but localization accuracy may deteriorate
Solution Approach 1:
The patent transforms environmental data through multiple parameter changes: converting raw sensor data into feature representations, applying compression algorithms to reduce data dimensionality, and using machine learning models to extract essential characteristics. These parameter transformations maintain localization accuracy while significantly improving computational efficiency by processing compressed rather than raw data
Solution Approach 2:
The patent introduces intermediate feature representations as mediators between raw environmental data and localization decisions. These feature representations serve as compressed intermediaries that retain essential spatial and contextual information while reducing data volume, enabling efficient processing without sacrificing localization precision
2Measurement precision
If detailed feature representations are used, then localization accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system dynamically adjusts representation detail levels through parameter changes, using compression algorithms and machine learning models to transform detailed environmental data into optimized feature representations that maintain necessary accuracy while reducing computational resource consumption for processing and storage
Solution Approach 2:
The patent segments environmental data into distinct feature representations that can be processed independently. By dividing the environment into manageable feature segments and processing only relevant portions, the system achieves accurate localization with reduced computational resource consumption compared to processing complete detailed representations
3Quantity of substance
If compression is applied to feature representations, then data storage efficiency improves, but information loss may occur
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates localization performance and adjusts compression parameters accordingly. By monitoring localization accuracy and feeding this information back to the compression process, the system optimizes compression levels to minimize information loss while maximizing storage efficiency
Solution Approach 2:
The system applies parameter changes through machine learning-based compression that adapts to the specific characteristics of environmental data. These intelligent parameter adjustments preserve critical spatial and contextual features during compression, reducing information loss compared to traditional compression methods while improving storage efficiency
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices associated with object localization and generation of compressed feature representations are provided. For example, a computing system can access source data and target data. The source data can include a source representation of an environment including a source object. The target data can include a compressed target feature representation of the environment. The compressed target feature representation can be based on compression of a target feature representation of the environment produced by machine-learned models. A source feature representation can be generated based on the source representation and the machine-learned models. The machine-learned models can include machine-learned feature extraction models or machine-learned attention models. A localized state of the source object with respect to the environment can be determined based on the source feature representation and the compressed target feature representation.