Vehicle AR-HUD Occlusion Detection for Selective Safety Augmentation
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Solution Overview
Problem
Existing in-vehicle augmented reality head-up displays (AR-HUDs) often overwhelm occupants with too much information, lacking an effective automated system to prevent over-augmentation and ensure relevant data is displayed without occlusion.
Innovation Solution
A system and method that utilize sensors, such as cameras and neural networks, to identify regions of interest related to vehicle safety and occupant demand, determining occlusion and selectively augmenting information on the AR-HUD when necessary, using a controller and display system to graphically remove occlusions or display graphics on the windscreen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AR-HUD displays more augmentation information to enhance environmental awareness, then occupant awareness is improved, but visual overload occurs and occupant comfort deteriorates
Solution Approach 1:
The system applies different augmentation qualities to different regions of interest based on their occlusion status and safety relevance. Critical occluded objects receive augmentation while non-critical or non-occluded objects do not, creating localized information enhancement without global visual overload.
Solution Approach 2:
The system performs partial augmentation by selectively displaying information only for occluded regions of interest rather than augmenting all detected objects. This partial action approach provides sufficient information for safety-critical occluded objects while avoiding the harm of excessive information display.
2Loss of information
If AR-HUD displays all detected information to provide comprehensive environmental data, then information completeness is improved, but display complexity increases and overwhelms the occupant
Solution Approach 1:
The system extracts and displays only the essential information related to occluded regions of interest from the complete set of detected environmental data. By taking out only the critical occluded objects for augmentation, the system maintains information completeness for safety-critical elements while reducing display complexity.
Solution Approach 2:
The system segments the environmental information into distinct categories (occluded vs. non-occluded, safety-critical vs. non-critical) and applies selective augmentation only to the necessary segments. This segmentation allows comprehensive information processing while maintaining simple display output.
3Reliability
If AR-HUD augments all regions of interest to ensure no critical information is missed, then safety is improved, but visual clutter increases and reduces driver focus
Solution Approach 1:
The system applies augmentation locally only to occluded regions of interest rather than uniformly to all regions. This localized approach ensures safety by highlighting missed critical information while avoiding visual clutter from augmenting already-visible objects.
Solution Approach 2:
The system performs partial augmentation only when necessary (for occluded objects) rather than excessive augmentation of all objects. This partial action maintains safety by ensuring occluded critical information is communicated while avoiding the harm of visual clutter from unnecessary augmentations.
Data Source
AI summary
A system and method for selectively augmenting information in a vehicle includes receiving a scene information from at least one sensor. identifying a region of interest from the scene information, analyzing the scene information to determine whether the region of interest is occluded, and augmenting the region of interest using a display system when the region of interest is occluded. The region of interest includes an object identified within the scene information that is classified as related to vehicle safety.