Driving Scene Perceptual Load Estimation Using Visual Features
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Solution Overview
Problem
Current methods fail to accurately measure and recognize perceptual load directly from the visual driving scene in a non-intrusive manner, which is essential for improving driving safety and predicting a driver's ability to detect safety-critical events, as they either require intrusive measures or amalgamate workload sources, making it difficult to isolate perceptual load.
Innovation Solution
A control device that extracts scene features from the visual driving scene and determines perceptual load using a data-driven approach with crowd-sourced load labels, trained on reference video scenes, allowing for real-time analysis without interfering with the driver's activities, employing optical sensors and machine learning models like regression or classification to map scene features to load values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intrusive measures are used to measure driver workload, then measurement precision may improve, but ease of operation deteriorates as it interferes with driver activities
Solution Approach 1:
The patent uses the visual driving scene as an intermediary to indirectly measure perceptual load. Instead of directly measuring driver state through intrusive means, the system analyzes environmental features (traffic density, scene complexity, object counts) that correlate with perceptual load, thereby avoiding interference with driver operations while still achieving measurement precision
Solution Approach 2:
The patent replaces direct physiological or behavioral measurement mechanisms with an optical sensing and computational analysis system. By substituting mechanical/intrusive measurement methods with optical scene analysis and machine learning algorithms, the system achieves accurate perceptual load assessment without physical interference with the driver
2Ease of manufacture
If workload sources are amalgamated, then ease of manufacture improves, but measurement precision deteriorates as it becomes difficult to isolate perceptual load
Solution Approach 1:
The patent segments the workload measurement task by focusing exclusively on perceptual load components related to the visual driving scene. Rather than amalgamating all workload sources, the system isolates and measures only scene-related perceptual demands using specific features like traffic density, scene complexity, and object counts, thereby maintaining measurement precision while simplifying the implementation scope
3Productivity
If real-time analysis is implemented, then productivity improves, but device complexity increases due to computational requirements
Solution Approach 1:
The patent applies partial action by selecting only the most relevant scene features for analysis (traffic density, scene complexity, object counts) rather than processing all possible visual information. This selective feature extraction enables real-time perceptual load determination with reduced computational complexity, achieving productivity improvement without excessive device complexity
Data Source
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AI summary
The invention relates to a control device (1) for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: • receive a sensor output (101) of a sensor (3), the sensor (3) sensing the visual driving scene, · extract a set of scene features (102) from the sensor output (101), the set of scene features (102) representing static and/or dynamic information of the visual driving scene, and • determine the perceptual load (104) of the set of extracted scene features (102) based on a predetermined load model (103), wherein the load model (103) is predetermined based on reference video scenes each being labelled with a load value The invention further relates to a system and a method.