Visual Sonar Geometry Adaptation for Vehicles With Attachments
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Solution Overview
Problem
Vehicle assistance systems face challenges in adapting to changes in vehicle dimensions due to external attachments, leading to increased collision risks and safety concerns, particularly in urban environments.
Innovation Solution
A system utilizing monocular depth estimation (MDE) algorithms and cameras to generate depth maps, recognizing attached objects, and adjusting vehicle dimensions to provide real-time feedback and avoid collisions by dynamically modifying vehicle geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vehicle assistance systems use fixed vehicle dimensions, then the system complexity is low, but the system cannot adapt to external attachments causing collision risks
Solution Approach 1:
The system dynamically adjusts the vehicle's effective dimensions by detecting external attachments and modifying the boundary representation in real-time. The processor identifies attached objects and generates an adapted vehicle boundary that extends beyond the physical vehicle boundaries to include protruding attachments, enabling the assistance system to adapt to changing vehicle geometry without hardware modifications.
Solution Approach 2:
The patent replaces physical mechanical measurement systems with computer vision and image processing techniques. By using cameras to capture images and algorithms to detect external attachments, the system substitutes complex mechanical dimensioning systems with optical sensing and computational analysis, reducing mechanical complexity while improving adaptability.
2Reliability
If the system dynamically modifies vehicle dimensions to adapt to attachments, then collision risks are reduced, but the device complexity increases
Solution Approach 1:
The system performs self-adjustment by automatically detecting external attachments and modifying its own operational parameters (vehicle boundary representation) without external intervention. The processor continuously monitors images, identifies attachments, and updates the vehicle boundary model autonomously, enabling the system to serve itself in adapting to changing conditions.
Solution Approach 2:
The system changes the boundary parameter of the vehicle model dynamically based on detected attachments. When external objects are identified, the processor modifies the vehicle boundary coordinates to reflect the extended effective dimensions, allowing the assistance system to maintain accurate collision avoidance parameters without physical modifications to the vehicle.
3Measurement precision
If the system uses standard vehicle boundaries, then the measurement process is simple, but the measurement precision is insufficient for vehicles with attachments
Solution Approach 1:
The system performs preliminary detection of external attachments before conducting distance measurements or providing assistance information. By first identifying protruding objects and adjusting the vehicle boundary model in advance, the system ensures that subsequent measurements are based on accurate, attachment-inclusive dimensions, improving overall measurement precision.
Solution Approach 2:
The patent introduces an intermediate processing layer between image capture and measurement output. The processor acts as an intermediary that detects attachments, adjusts boundary parameters, and then uses these adapted boundaries for measurements. This intermediate step reconciles the simplicity of standard measurement processes with the need for high precision in vehicles with varying configurations.
Data Source
AI summary
Embodiments of systems and methods for adapted vehicle geometry include a vehicle and one or more processors. The vehicle includes a camera operable to generate an image of an environment surrounding the vehicle. The environment includes a parking space and an object removably attached to the vehicle. The one or more processors operable to identify the object as attached to the vehicle, generate, using a pre-trained depth algorithm, a depth map based on the image, generate a boundary of the parking space and a boundary of the vehicle combined with the object based on the depth map, determine whether a distance between the boundary of the parking space and the boundary of the vehicle combined with the object is less than a threshold value, and output an alert in response to determining that the distance is less than the threshold value.