Cross-Sensor Feature Mapping for Vehicle Object Detection
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Solution Overview
Problem
Existing sensor systems for vehicle navigation struggle to effectively compare and utilize data from different sensor types, leading to inefficiencies and inaccuracies in object detection and localization in vehicular environments.
Innovation Solution
A sensor-agnostic detection system that extracts high-level features from survey data and maps them to a feature-based map, allowing for the correlation and identification of objects using input data from dissimilar sensor types, enabling cross-sensor compatibility and improved detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data from different sensor types is directly compared for object detection, then detection coverage is improved, but measurement precision deteriorates due to incompatibility between sensor data formats and characteristics
Solution Approach 1:
The patent introduces a feature extraction module as an intermediary that converts raw sensor data from different sensor types into standardized high-level features. This mediator layer enables comparison between incompatible sensor data formats while maintaining detection precision, resolving the contradiction between versatility and measurement accuracy.
Solution Approach 2:
The system transforms sensor data from its original physical parameters (e.g., light intensity, electromagnetic waves) into unified feature parameters through extraction and mapping processes. This parameter transformation allows diverse sensor types to contribute to object detection in a compatible manner, improving detection coverage without sacrificing precision.
2Measurement precision
If high-quality sensors are used for accurate object detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The feature extraction and mapping system provides multi-functionality by enabling a single sensor to serve multiple detection purposes through standardized feature representation. This universal approach allows the system to achieve high detection accuracy without requiring specialized high-quality sensors for each function, thereby reducing device complexity.
Solution Approach 2:
The system creates a virtual copy of the environment through feature-based maps that represent objects and their characteristics. This copying approach allows accurate object detection by comparing sensor data against feature representations rather than requiring direct high-precision sensor measurements, reducing the need for expensive specialized sensors.
3Measurement precision
If sensor-specific data processing is used, then measurement precision for that sensor type is improved, but adaptability to other sensor types deteriorates
Solution Approach 1:
The system segments the object detection process into distinct modules: sensor-specific feature extraction, universal feature mapping, and object identification. This segmentation allows each sensor type to be processed with its optimal method while contributing to a unified detection framework, maintaining both precision and adaptability.
Solution Approach 2:
The feature mapping module serves as an intermediary that translates sensor-specific features into a universal representation format. This mediator enables cross-sensor compatibility while preserving the measurement precision benefits of sensor-specific processing, as each sensor's unique characteristics are captured and then standardized for comparison.
Data Source
AI summary
The systems and methods described herein disclose detecting objects in a vehicular environment using extracted high level features. As described here, a survey vehicle detects objects in the environment with a survey sensor. Vehicles can then use the extracted features from the survey sensor data to compare with extracted features from input sensor data. Thus, vehicles can benefit from different sensor data without employing said sensor. The systems and methods can include extracting one or more survey features from a survey data set, which can then be mapped to a feature-based map of an environment. An input data set can then be collected using an input sensor type with one or more input features being extracted from the input data set. The one or more input features can then be correlated to the one or more survey features to identify an object in the environment.