Focus-Based Sensor Data Tagging for Autonomous Vehicle Hazard Detection
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Solution Overview
Problem
Current approaches for training machine learning models in autonomous vehicles are computationally expensive and time-intensive due to the need to process entire scenes from three-dimensional or two-dimensional sensors, lacking efficient methods to identify regions of interest and provide feedback to the learning algorithm.
Innovation Solution
The system generates focus records by analyzing driver gaze direction and sensor data to prioritize regions of interest, using these records to train a machine learning model that focuses on areas of particular importance, reducing computational load and improving hazard detection by mimicking human driver behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the machine learning model processes entire scenes from sensors to identify hazards, then hazard detection accuracy is maintained, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the sensor scene into multiple regions of interest based on driver gaze direction and contextual factors. Instead of processing the entire scene uniformly, the system divides it into focal regions (where the driver is looking) and peripheral regions, applying different processing priorities and computational resources to each segment. This segmentation maintains hazard detection accuracy in critical areas while reducing overall computational load and processing time.
Solution Approach 2:
The patent applies local quality by enhancing processing resolution and computational effort specifically in regions of interest determined by driver gaze, while using reduced processing in less critical areas. The system dynamically adjusts the quality and depth of analysis based on the importance of each scene region, ensuring high accuracy where needed while optimizing overall system efficiency.
2Reliability
If the system processes all sensor data comprehensively to ensure safety, then hazard detection reliability is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements dynamic processing that adapts computational resource allocation based on real-time driving conditions, driver behavior, and scene complexity. The system dynamically adjusts which regions receive intensive processing versus standard processing, optimizing the balance between reliability and computational cost. This dynamic approach ensures high reliability for critical hazard detection while reducing unnecessary computational expenditure in low-risk scenarios.
Solution Approach 2:
The system uses driver gaze direction as a self-provided cue to automatically identify regions requiring intensive processing. The driver's natural attention patterns serve as an internal signal that guides computational resource allocation, eliminating the need for external intervention or overly comprehensive processing of all scene areas. This self-service mechanism optimizes resource usage while maintaining safety.
3Area of stationary object
If the machine learning model analyzes the complete sensor scene, then detection coverage is maximized, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-identifying regions of interest using driver gaze direction and contextual information before performing detailed hazard analysis. This preliminary segmentation step guides subsequent processing, allowing the system to focus computational complexity only on relevant areas while maintaining comprehensive detection coverage. The pre-processing step simplifies the overall task by eliminating the need for exhaustive analysis of all scene regions.
Data Source
AI summary
Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.