Parallel Vehicle Models for Changing Light Conditions
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Solution Overview
Problem
Vehicles face challenges in accurately navigating due to varying environmental conditions, such as changes in ambient light levels, which affect the quality and clarity of captured image data, leading to inconsistent performance of machine learned models.
Innovation Solution
Implementing multiple machine learned models configured for different environmental conditions, running in parallel, and selecting the output with the highest confidence score based on real-time sensor data to adjust for changes in ambient lighting and other conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine learned model is used for vehicle navigation, then the system complexity is low, but the accuracy and reliability of object detection deteriorates under varying environmental conditions
Solution Approach 1:
The patent divides the navigation system into multiple specialized machine learned models, each trained for specific environmental conditions (e.g., daytime, nighttime, rainy, snowy). This segmentation allows each model to excel in its designated condition, improving overall reliability while managing complexity through modular architecture where models can be selected based on current environmental sensors.
Solution Approach 2:
The patent creates a universal navigation system that can adapt to multiple environmental conditions by implementing an ensemble of models with different expertise. The system universally handles various conditions (day, night, rain, snow) by selecting or combining appropriate models, making the overall system multi-functional rather than condition-specific.
2Reliability
If multiple machine learned models are run in parallel for different environmental conditions, then the accuracy under varying conditions improves, but the computational resources and processing time increase
Solution Approach 1:
The patent implements a dynamic model selection mechanism that adapts to current environmental conditions in real-time. Based on sensor inputs (light sensors, weather sensors), the system dynamically selects only the relevant models for execution, avoiding the constant operation of all models. This dynamic approach maintains high accuracy under varying conditions while significantly reducing computational resource usage compared to running all models continuously.
Solution Approach 2:
The system changes operational parameters (which models are active) based on environmental conditions. Instead of running all models at full capacity regardless of conditions, the system adjusts the active model set according to detected environment, reducing computational load while maintaining accuracy. For example, nighttime models are deactivated during daytime operation.
3Adaptability or versatility
If environmental condition detection is implemented to select appropriate models, then the adaptability improves, but the system complexity and sensor requirements increase
Solution Approach 1:
The patent implements preliminary environmental assessment using sensors (light, weather) before model selection. The system proactively detects environmental conditions and pre-selects the appropriate model subset, allowing smooth transitions between conditions. This preliminary action enables high adaptability while managing complexity through a structured, rule-based selection process rather than complex real-time switching.
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.