Geographic Model Adaptation for In-Vehicle Feature Detection
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Solution Overview
Problem
In-vehicle feature detectors for autonomous driving face challenges in balancing feature detection performance and resource constraints, requiring significant computing resources that are not adequately met by traditional methods, especially in resource-limited embedded systems.
Innovation Solution
A dynamic adaptation method is introduced, where precomputed weights for feature detection models are retrieved and deployed based on the vehicle's geographic location and operational conditions, allowing the in-vehicle feature detector to adapt by matching detected features with mapped features in the map data to determine the current location, thereby optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional feature detection methods are used, then feature detection capability is provided, but computing resource requirements are excessively high for in-vehicle embedded systems
Solution Approach 1:
The patent segments the feature detection system into multiple components: a cloud-based training system that creates detection models, and an in-vehicle detector that executes these models. The detection models are further segmented into multiple scales (e.g., different object sizes) and processed in parallel. This segmentation allows complex detection tasks to be distributed across multiple smaller processing units, reducing the computational burden on any single in-vehicle processor while maintaining comprehensive detection capability.
Solution Approach 2:
The patent implements preliminary action by pre-training feature detection models in the cloud using extensive datasets before deploying them to in-vehicle systems. The models are pre-processed, pre-optimized, and prepared in advance, so that during actual vehicle operation, the detector can execute predictions with minimal real-time computational overhead. This shifts the heavy computational workload from the resource-constrained in-vehicle system to the more powerful cloud training environment.
2Reliability
If the number and variety of detectable features are increased, then detection performance is improved, but computing resource requirements increase further
Solution Approach 1:
The patent divides the detection of multiple feature types and scales into separate parallel processing streams. Each stream handles specific feature categories or size ranges independently, allowing the system to detect a wide variety of features without requiring all detection logic to run sequentially on a single processor. This parallel segmentation enables comprehensive detection performance while managing computational complexity through distributed processing.
Solution Approach 2:
The patent develops universal feature detection models that can detect multiple types of features (pedestrians, vehicles, cyclists, etc.) across various scales using a single trained model architecture. Rather than requiring separate specialized detectors for each feature type, the universal model performs multi-class detection, reducing the overall system complexity while maintaining the ability to detect diverse features simultaneously.
3Device complexity
If a single feature detection model is used for all geographic areas, then device complexity is reduced, but detection accuracy varies across different environments
Solution Approach 1:
The patent implements local quality by training and deploying region-specific feature detection models tailored to different geographic areas, environmental conditions, and feature distributions. Each region receives a customized model optimized for its local characteristics (e.g., urban vs. rural environments, different lighting conditions, region-specific objects). This allows the system to maintain high detection accuracy across diverse environments while the underlying architecture remains consistent, balancing customization with manageable complexity.
Data Source
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AI summary
An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.