Driving Scene Attention Mapping for Real-Time Perceptual Load
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Solution Overview
Problem
Current methods for determining perceptual load in driving scenes rely on subjective estimates or post-event analysis, failing to anticipate critical events due to inattention caused by high or low information loads, which can lead to accidents.
Innovation Solution
A control device that calculates perceptual load directly from the driving scene using image sequences, extracting scene features, generating attention maps, and determining attentional demand in real-time through attentional pooling, allowing for real-time prediction and correlation with human ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time perceptual load determination is implemented using automated processing, then driving safety is improved, but device complexity increases
Solution Approach 1:
The visual driving scene is divided into multiple regions of interest (ROIs) based on scene features extracted from image sequences. The system segments the complex visual information into manageable components (e.g., road, vehicles, pedestrians, obstacles) and processes each segment separately to determine perceptual load, thereby improving safety without overwhelming the processing system
Solution Approach 2:
The system transforms the two-dimensional visual scene into a multi-dimensional analysis by incorporating temporal dimension (video sequences), spatial dimension (region segmentation), and feature dimension (multiple scene attributes). This dimensional expansion enables comprehensive perceptual load assessment while distributing computational complexity across different processing dimensions
2Measurement precision
If comprehensive scene feature extraction is performed, then measurement precision of perceptual load is improved, but processing time increases
Solution Approach 1:
The system performs preliminary extraction of key scene features (objects, obstacles, road conditions) from video frames before conducting the full perceptual load analysis. By pre-processing and identifying critical elements in advance, the system reduces the computational burden during real-time assessment while maintaining measurement precision
Solution Approach 2:
The system applies different processing quality levels to different regions of the scene based on their importance. High-precision feature extraction is applied to critical regions (e.g., nearby obstacles, pedestrians), while lower-precision processing is used for less critical areas, thereby balancing measurement accuracy with processing efficiency
3Loss of information
If attention maps are calculated through attentional pooling, then information load management is improved, but device complexity increases
Solution Approach 1:
The system extracts and isolates the essential attention-related information from the complex visual scene by calculating attention maps through attentional pooling. This extraction process separates critical attentional demand signals from the overwhelming visual data, improving information load management while using standardized neural network operations to control complexity
Data Source
AI summary
A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.