Vehicle Perception Model Switching for Changing Environmental Conditions
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Solution Overview
Problem
Vehicles face challenges in accurately navigating due to varying environmental conditions, as the quality of captured data can change significantly based on factors like ambient light levels, weather, and time of day, affecting the reliability of image data processing.
Innovation Solution
Implementing multiple machine learned models configured for different environmental conditions, allowing them to be run in parallel and selecting the output with the highest confidence score, and adjusting model configurations to compensate for changes in environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learned model is used for all environmental conditions, then the device complexity is low, but the measurement precision and reliability of data capture deteriorate under varying environmental conditions
Solution Approach 1:
The patent divides the single model approach into multiple specialized machine learned models, each trained for specific environmental conditions (e.g., daytime, nighttime, adverse weather). This segmentation allows each model to optimize for its designated condition, improving measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The system dynamically selects and switches between different model configurations based on real-time environmental condition detection. Sensors monitor ambient light, weather, and other conditions to determine which model configuration is currently most appropriate, allowing the system to adapt its processing pipeline to maintain high data quality across varying conditions.
2Reliability
If multiple machine learned models are run in parallel to handle different environmental conditions, then the reliability and accuracy improve, but the device complexity and computational resources increase
Solution Approach 1:
Multiple machine learned models are segmented into distinct configurations, each specialized for particular environmental conditions. This allows parallel processing of different data streams through appropriate models, improving reliability by ensuring each model operates in its optimal condition range while maintaining manageable complexity through clear functional separation.
Solution Approach 2:
The system changes operational parameters by selecting different model configurations based on environmental conditions. Rather than running all models simultaneously with equal weight, the system adjusts which models are active and how their outputs are weighted, optimizing the balance between reliability and computational complexity through parameter adaptation.
3Measurement precision
If model configurations are adjusted to compensate for environmental changes, then the measurement precision improves, but the ease of operation and system management become more difficult
Solution Approach 1:
The system implements self-service through automated environmental condition monitoring and model selection. Sensors continuously detect ambient conditions and automatically determine the appropriate model configuration without requiring manual intervention. This maintains high measurement precision while simplifying operation, as the system self-adjusts based on real-time environmental feedback.
Solution Approach 2:
A feedback loop continuously monitors environmental conditions and adjusts model configuration selection accordingly. The system uses sensor data about ambient light, weather, and other conditions to feedback into the model selection process, automatically optimizing measurement precision while eliminating the need for manual model management and maintaining ease of operation.
Data Source
AI summary
Techniques for adjusting vehicle models based on environmental conditions are discussed herein. The techniques may include receiving image data representing a portion of an environment in which a vehicle is operating and inputting the image data into a machine learned model. Additionally, data representing an environmental condition associated with the environment may be received from a sensor of the vehicle to detect changes in the environmental conditions such that one or more actions associated with the machine learned model or an output of the machine learned model may be performed. Some of the techniques may also include running multiple machine learned models or multiple configurations of a machine learned model in parallel and selecting different outputs of the machine learned model(s) to adjust for changes in the environmental conditions. For instance, individual outputs may be selected based on environmental conditions, confidence scores, thresholds, etc.