Autonomous Vehicle Component Snapshots for Rapid Evaluation
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Solution Overview
Problem
Conventional system evaluation methods for autonomous vehicles are inefficient, requiring significant runtime and data to simulate scenarios and evaluate component performance, making it difficult to isolate changes in system performance and behavior.
Innovation Solution
The technology employs snapshots, which are serialized sets of inputs capturing a component's state, allowing for rapid evaluation by serializing and deserializing state information, enabling quick testing of scenarios and components in isolation with minimal latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional simulation methods are used to evaluate autonomous vehicle components, then comprehensive system evaluation can be achieved, but significant runtime and computational resources are required
Solution Approach 1:
The patent divides the autonomous vehicle system into discrete, independently testable components. Each component can be evaluated through targeted unit tests rather than requiring full-system simulations, significantly reducing evaluation time while maintaining reliability.
Solution Approach 2:
The patent creates simplified copies of component behaviors and states that can be tested in isolation. These copies capture essential component characteristics without requiring the full complexity of the original system, enabling rapid evaluation.
2Measurement precision
If comprehensive scenario data is collected for simulation, then accurate system evaluation is possible, but data requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential data elements needed to evaluate specific component behaviors, rather than processing comprehensive scenario data. This extraction approach maintains evaluation accuracy while dramatically reducing data processing complexity.
Solution Approach 2:
The patent applies partial action by collecting and processing only the subset of data necessary for evaluating particular component aspects, rather than processing all available scenario data. This reduces complexity while maintaining sufficient precision for component-level evaluation.
3Reliability
If full system simulations are run for different scenarios, then overall system performance can be assessed, but it becomes difficult to isolate specific component issues
Solution Approach 1:
The patent segments the evaluation process into component-specific unit tests, allowing each component to be tested independently. This segmentation makes it straightforward to identify which specific component is causing performance issues without the confounding factors present in full-system simulations.
Solution Approach 2:
The patent introduces component interfaces and test harnesses as intermediaries that isolate individual components from the full system complexity. These intermediaries enable precise measurement and detection of component-specific issues while maintaining the ability to assess overall system performance.
4Productivity
If rapid component evaluation is implemented through snapshots, then testing efficiency improves, but data serialization and storage requirements are introduced
Solution Approach 1:
The patent creates serialized copies of component states (snapshots) that can be rapidly stored and replayed for testing. These copies capture only the essential state information needed for reproduction, enabling rapid evaluation while keeping storage requirements manageable.
Solution Approach 2:
The patent transforms component state data into serialized parameter representations that are more compact and efficient for storage and transmission. This parameter transformation maintains the ability to reconstruct component states while reducing the quantity of data that must be stored.
Data Source
AI summary
The technology involves evaluating components of an autonomous vehicle that can be meaningfully evaluated over a single operational iteration. One or more snapshots of single iteration scenarios can be tested quickly and efficiently, either on vehicle or via a back-end system. Each snapshot corresponds to a particular point in time when a given component runs. Each snapshot contains a serialized set of inputs necessary to evaluate the particular component. These inputs comprise the minimal amount of information needed to accurately and faithfully recreate what the component did or does. Each snapshot is triggered at the particular point in time based on one or more criteria associated with either a driving scenario or a component of the vehicle during autonomous driving. A serialized snapshot may be retrieved from storage and deserialized, so that the system may evaluate the state of the component at the particular point in time.