Geolocalized Perception Models for Unusual Road Environments
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Solution Overview
Problem
Generalized machine-learning models struggle to recognize and respond appropriately to unusual vehicle environments, such as those with unique features like diagonal crosswalks, due to overfitting or underfitting, leading to incorrect inferences and inefficient vehicle operations.
Innovation Solution
Dividing the map into region-specific areas and training models for each region, allowing the vehicle to switch between models when entering different areas, thereby using region-specific models to handle unusual environments without affecting performance in typical areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single generalized machine-learning model is used for all environments, then the model structure remains simple and computationally efficient, but the model performs poorly in unusual environments due to overfitting or underfitting
Solution Approach 1:
The patent divides the operating environment into multiple regions (e.g., typical environments and unusual environments) and trains separate machine-learning models for each region. This segmentation allows each model to be specialized for its specific environment, improving inference accuracy in unusual environments while maintaining manageable complexity through regional specialization rather than requiring a single complex universal model.
Solution Approach 2:
The patent applies local quality by training region-specific models with parameters optimized for local environmental characteristics. Each region has its own model parameters that are locally adapted to that environment's unique features, allowing the system to handle environmental diversity without requiring a single overly complex global model.
2Reliability
If region-specific models are trained for different environments, then inference accuracy in unusual environments improves, but the system complexity and computational resources required increase
Solution Approach 1:
By segmenting the environment into distinct regions and assigning specialized models to each, the system manages complexity through structured division rather than attempting to handle all environments with a single model. This makes the multi-model system more tractable and easier to maintain compared to a monolithic approach.
Solution Approach 2:
The patent introduces an environment classification mechanism that acts as an intermediary to determine which region-specific model should be used for a given input. This mediator layer simplifies the overall system architecture by providing a clear decision-making pathway for model selection, reducing the operational complexity of managing multiple specialized models.
3Adaptability or versatility
If multiple region-specific models are used, then the system can adapt to diverse environmental conditions, but the time and computational resources required for model switching and management increase
Solution Approach 1:
The patent performs preliminary classification of the operating environment to determine which region-specific model should be used before actual inference is performed. This advance determination allows the system to pre-load or pre-activate the appropriate model, minimizing the time required for model switching during actual operation and reducing computational overhead during inference.
Solution Approach 2:
The system utilizes parameter changes in environmental conditions as triggers for model selection. By monitoring key environmental parameters and comparing them against region definitions, the system can efficiently determine when to switch models based on parameter thresholds, reducing the computational burden of continuous model evaluation and speeding up the adaptation process.
Data Source
AI summary
In one embodiment, a method includes, by a computing system associated with a vehicle, determining a current location of the vehicle in a first region, identifying one or more first sets of model parameters associated with the first region and one or more second sets of model parameters associated with a second region, generating, using one or more machine-learning models based on the first sets of model parameters, one or more first inferences based on first sensor data captured by the vehicle, switching the configurations of the models from the first sets of model parameters to the second sets of model parameters, generating, using the models having configurations based on the second sets of model parameters, one or more second inferences based on second sensor data generated by the sensors of the vehicle in the second region, and causing the vehicle to perform one or more operations based on the second inferences.