Vehicle Contextual Complexity Metric Spatial Filtering
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Solution Overview
Problem
Autonomous vehicles face high costs due to extensive demonstration training and may not cover all possible driving scenarios, limiting their effectiveness in adapting to varying contextual environments.
Innovation Solution
A vehicle system that determines the complexity of its contextual environment using sensor-generated output signals, filters this information spatially based on a complexity metric, and adjusts its operations accordingly without external description data, using machine-readable instructions and components like sensors, a complexity component, and a controller to focus on specific or general contextual information for appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use extensive demonstration training to learn and operate, then the vehicle's operational capability is improved, but the training cost and time increase significantly
Solution Approach 1:
The system pre-processes sensor data during normal operation to build contextual environment models and complexity metrics in advance, so that when decision-making is needed, the vehicle can quickly reference pre-analyzed information rather than processing raw sensor data from scratch during critical moments
Solution Approach 2:
The vehicle performs self-supervised learning by automatically analyzing its own sensor-generated output signals to determine contextual information and complexity metrics, eliminating the need for extensive external demonstration training data and reducing dependency on manual training interventions
2Reliability
If autonomous vehicles process all contextual information to ensure comprehensive awareness, then the vehicle's situational awareness is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system applies spatial filtering to process different regions of the contextual environment with different levels of detail, focusing computational resources on areas of high relevance or potential risk while using coarser processing for less critical regions, thereby reducing overall computational complexity while maintaining essential situational awareness
Solution Approach 2:
The complexity metric dynamically adjusts processing parameters such as filter resolution, detection thresholds, and attention focus based on the current environmental context, allowing the system to optimize computational resource allocation by intensifying processing only when and where complexity demands it
3Measurement precision
If autonomous vehicles use spatial filtering to focus on specific contextual information, then the response accuracy is improved, but the risk of missing important information increases
Solution Approach 1:
The spatial filtering parameters are dynamically adjusted based on the determined complexity metric, allowing the system to adaptively focus on specific contextual information when complexity is high while maintaining broader awareness when complexity is low, thus balancing response accuracy with information completeness in varying operational contexts
Data Source
AI summary
Exemplary implementations may: generate output signals conveying contextual information and vehicle information; determine, based on the output signals, the contextual information; determine, based on the output signals, the vehicle information, determine, in an ongoing manner, based on the contextual information and/or the vehicle information, values of a complexity metric, the complexity metric quantifying predicted complexity of a current contextual environment and/or predicted complexity of a likely needed response to a change in the contextual information; filter, based on the values of the complexity metric, the contextual information spatially; and control, based on the vehicle information and the spatially filtered contextual information, the vehicle such that the likely needed response is satisfied.