AR Blind-Area Visualization for In-Vehicle Sensor FOV Limits
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Solution Overview
Problem
Existing autonomous vehicles lack effective methods to visualize and address sensor blind areas, which can lead to unexpected situations requiring manual driver intervention, especially in complex traffic scenarios.
Innovation Solution
A method and system that predicts blind areas based on sensor ranges and field-of-view data, using augmented or mixed reality displays to visualize these areas, and adjusts driving modes accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate without visualizing sensor blind areas, then the system complexity is reduced, but driver awareness and safety are compromised
Solution Approach 1:
The patent introduces an AR/MR display device as an intermediary between the vehicle's sensor system and the driver. This mediator visualizes blind areas by overlaying graphical representations on the driver's field of view, translating complex sensor data into intuitive visual information without requiring direct interaction with the sensor system itself
Solution Approach 2:
The patent replaces traditional mechanical or physical warning systems (such as audible alerts or dashboard indicators) with optical field-based AR/MR visualization. This substitution uses light and visual processing to convey sensor blind area information, providing more precise and context-aware feedback to the driver
2Reliability
If the vehicle continuously monitors and updates blind areas along the predicted route, then the reliability of autonomous driving is improved, but the energy consumption increases
Solution Approach 1:
The patent performs preliminary analysis of blind areas along the predicted driving route before the vehicle reaches those locations. By pre-calculating and pre-visualizing potential blind areas using map data and sensor models, the system prepares safety information in advance rather than continuously processing real-time data throughout the entire route
Solution Approach 2:
The patent updates blind area visualizations periodically based on significant changes in vehicle state (such as route changes, sensor recalibration events, or reaching predefined distance milestones) rather than continuously. This periodic updating maintains reliability by providing timely information while reducing unnecessary computational and energy overhead
3Reliability
If the system provides detailed AR visualization of blind areas, then driver awareness is enhanced, but the ease of operation is reduced due to potential distraction
Solution Approach 1:
The patent applies visualizations with varying levels of detail and prominence based on the specific characteristics of each blind area. Critical blind areas (those posing higher risk) receive more prominent visualization, while less critical areas receive subtler indicators. This localized differentiation enhances awareness of important hazards while minimizing distraction from less significant information
Solution Approach 2:
The patent selectively visualizes only the most relevant blind areas rather than providing complete coverage of all sensor limitations. By focusing on partial information that is most critical for safe operation (excessive visualization of all details would cause distraction), the system enhances driver awareness of key hazards while maintaining ease of operation through reduced cognitive load
Data Source
AI summary
Some embodiments of a method disclosed herein may include: receiving a predicted driving route, sensor ranges of sensors on an autonomous vehicle (AV), and sensor field-of-view (FOV) data; determining whether minimum sensor visibility requirements are met along the predicted driving route; predicting blind areas along the predicted driving route, wherein the predicted blind areas are determined to have potentially diminished sensor visibility; and displaying an augmented reality (AR) visualization of the blind areas using an AR display device.