Vehicle Space Morphing With Time-of-Flight Point Clouds
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Solution Overview
Problem
Conventional techniques for modifying vehicle spaces lack accurate three-dimensional information and dynamic processes for optimizing space utilization, making it challenging to determine spatial availability effectively.
Innovation Solution
A detection system using time-of-flight sensors, such as LiDAR modules, generates three-dimensional point clouds of vehicle compartments and objects to be loaded, enabling precise calculation and comparison of spatial information for determining availability based on occupancy, object dimensions, and load distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used for modifying vehicle spaces, then the process is simpler, but the measurement precision and spatial availability assessment are insufficient
Solution Approach 1:
The patent replaces conventional mechanical measurement methods with optical sensing technology. Time-of-flight sensors emit light pulses and measure the time for reflected light to return, enabling non-contact, high-precision three-dimensional spatial measurement of vehicle compartments and cargo, thereby achieving accurate spatial availability assessment without complex mechanical measurement devices
Solution Approach 2:
The patent creates a digital three-dimensional point cloud copy of the physical vehicle compartment and cargo. The time-of-flight sensors generate precise spatial coordinates that form a digital model, allowing virtual simulation and analysis of space availability for different cargo configurations without physically rearranging items
2Measurement precision
If complex imaging methods are used, then the measurement precision improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex mechanical imaging systems with optical time-of-flight sensing. Instead of using multiple cameras or laser scanners requiring complex synchronization and calibration, the system uses time-of-flight sensors that directly measure distance through light pulse travel time, simplifying the imaging architecture while maintaining high three-dimensional spatial measurement precision
Solution Approach 2:
The patent changes the measurement parameter from intensity-based optical imaging to time-based distance measurement. By measuring the time of flight of light pulses rather than relying on image intensity and complex reconstruction algorithms, the system achieves accurate three-dimensional spatial information with simpler device architecture and reduced computational requirements
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
Enables efficient and dynamic optimization of vehicle space utilization by providing accurate spatial availability assessments for static, pseudo-static, and dynamic morphing configurations, reducing the need for complex imaging methods and enhancing computational efficiency.
Implementation Method 1
generating, via at least one time-of-flight sensor, a first point cloud representing the compartment of the vehicle. The first point cloud includes three-dimensional positional information of the compartment
Implementation Method 2
the at least one time-of-flight sensor includes a first LiDAR module in the vehicle configured to capture the first point cloud
Data Source
AI summary
A method for determining spatial availability for a compartment of a vehicle includes generating, via at least one time-of-flight sensor, a first point cloud representing the compartment of the vehicle, the first point cloud including three-dimensional positional information of the compartment. The method further includes calculating, via processing circuitry in communication with the at least one time-of-flight sensor, spatial information corresponding to a target configuration for the compartment. The method further includes comparing, via the processing circuitry, the spatial information to the first point cloud. The method further includes determining an availability of the target configuration based on the comparison of the spatial information to the first point cloud.