Vehicle HUD Alerts for Hidden Body Detection Using Light Fields
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Solution Overview
Problem
Existing vehicle systems fail to effectively identify and alert occupants to hidden objects within or near the vehicle's path, leading to potential accidents due to insufficient reaction time.
Innovation Solution
A system utilizing a front camera, augmented reality head-up display, and a trained neural network to detect hidden bodies through light field imaging, projecting real-time augmentation graphics on the windshield to draw the occupant's attention to these objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera systems are used to monitor the roadway, then the system structure remains simple, but hidden bodies obscured by other objects cannot be detected
Solution Approach 1:
The patent transitions from conventional 2D image capture to light field imaging that captures 4D information (x, y, z, and angular dimensions). This dimensional enhancement enables the system to detect hidden bodies by analyzing light ray directions and reconstructing three-dimensional spatial relationships, allowing objects obscured in conventional views to be identified through angular and depth information.
Solution Approach 2:
The patent introduces light field cameras and neural network algorithms as intermediary components between the physical scene and the detection output. The light field camera captures comprehensive optical information, and the neural network serves as an intermediary processing layer that analyzes this data to identify hidden bodies, bridging the gap between raw optical data and meaningful detection results.
2Loss of time
If the system waits for hidden bodies to become visible before alerting the driver, then the alert system remains simple, but the reaction time is insufficient to prevent accidents
Solution Approach 1:
The system performs preliminary detection of hidden bodies using light field imaging and neural network analysis before these objects become visible threats to the driver. By identifying concealed objects in advance and projecting warning graphics on the windshield, the system provides early alerting that gives drivers sufficient time to react and avoid accidents, rather than waiting until objects are directly visible.
Solution Approach 2:
The system implements a feedback loop where the neural network continuously analyzes light field data, identifies hidden bodies, and triggers augmented reality warnings on the windshield. This closed-loop feedback mechanism provides real-time information about concealed threats and monitors driver awareness, creating an proactive safety system that continuously adapts to the roadway environment.
3Loss of information
If augmentation graphics are displayed to alert the driver of hidden bodies, then driver awareness is improved, but the energy consumption of the display system increases
Solution Approach 1:
The augmented reality display system operates periodically rather than continuously, activating warning graphics only when the neural network detects hidden bodies that require driver attention. This periodic operation reduces energy consumption compared to continuous display operation, while still providing necessary information about concealed threats when they are identified by the detection system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the driver's awareness of hidden objects, allowing for timely reaction and preventing accidents by providing accurate and proactive alerts.
Implementation Method 1
mapping images captured by the at least one front camera to light field camera images stored within the trained neural network
Implementation Method 2
an augmented reality head up display in communication with a controller and adapted to project an image upon an inner surface of a windshield of the vehicle
Data Source
AI summary
A system for identifying hidden bodies and providing augmentation within a head up display of a vehicle including at least one front camera adapted to collect images of an environment in front of the vehicle within and adjacent to a roadway on which the vehicle is traveling, an augmented reality head up display adapted to project an image upon an inner surface of a windshield of the vehicle, an augmented reality display adapted to project an image within the windshield, the controller adapted to use a trained neural network stored therein to map images captured by the at least one front camera to light field camera images stored within the trained neural network and to identify hidden bodies within the images captured by the at least one front camera, and display, within a windshield of the vehicle, augmentation graphics for the identified hidden bodies.