Trailer Coupler Depth Mapping for Autonomous Hitch Alignment
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in efficiently processing sensor data to accurately identify and track a trailer hitch coupler for alignment, which is crucial for smooth vehicle maneuvers.
Innovation Solution
A method and system that convert camera images into depth maps to locate and track the trailer coupler by identifying points closest to a reference point, using sensor data for vehicle odometry and dynamical modeling, and communicating the coupler's position to the vehicle's driving control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera images are converted to depth maps and sensor data is processed to identify and track the trailer coupler, then the precision of coupler location and alignment is improved, but the complexity of the system increases due to multiple sensor systems and data processing requirements
Solution Approach 1:
The system divides the complex task of coupler identification into distinct processing stages: image capture from multiple cameras, depth map generation, region of interest selection, and coupler identification algorithms. This segmentation allows each module to be optimized independently while maintaining overall system precision.
Solution Approach 2:
Depth maps serve as an intermediary representation between raw camera images and coupler identification. The depth maps transform complex image data into simplified distance information, making coupler detection more accurate while reducing the computational complexity of direct image analysis.
2Speed
If real-time depth map creation and coupler tracking are implemented, then the speed of autonomous coupling operation is improved, but the energy consumption increases due to continuous sensor processing
Solution Approach 1:
The system applies local quality optimization by creating depth maps only for selected regions of interest rather than processing entire images. This approach maintains real-time coupling speed by focusing computational resources only on areas where the coupler may appear, significantly reducing energy consumption.
Solution Approach 2:
The system performs partial processing by generating depth maps selectively rather than continuously for the entire field of view. This partial action approach maintains sufficient coupling speed while avoiding the excessive energy consumption that would result from full-field continuous processing.
3Reliability
If multiple sensor systems are used to determine vehicle odometry and camera pose, then the reliability of autonomous operation is improved, but the difficulty of detecting and measuring increases due to data integration complexity
Solution Approach 1:
The system employs multi-functional sensor integration where a single processing framework handles data from multiple sensor types (cameras, GPS, accelerometers, wheel encoders). This universal approach improves reliability by cross-validating measurements while managing integration complexity through a unified data processing architecture.
Solution Approach 2:
The system uses feedback mechanisms where sensor data from multiple sources continuously updates the vehicle pose and coupler position estimates. This feedback loop improves autonomous operation reliability by detecting and correcting measurement errors, while the structured feedback architecture manages the complexity of integrating multiple sensor inputs.
Data Source
AI summary
A method and system for locating and tracking a trailer coupler for autonomous vehicle operation is disclosed. The system converts an image from a vehicle camera to a depth map that includes a plurality of points indicative of a distance between an object within the image and a reference point. The system used the depth map to identify and track a coupler of a trailer.