Docking Assistant Using Geometric Image Analysis for Truck Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle guidance systems rely on clearly visible lane markings, which can be obscured by soiling or wear, making it difficult to accurately determine the vehicle's position and orientation, especially when maneuvering trucks towards docking stations.
Innovation Solution
A method and device that utilize image data from an imaging sensor to extract positional parameters of potential destinations by edge detection, segmentation, and analysis of geometric objects, allowing for the calculation of an optimized travel path without the need for specific visual signatures at the destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If lane markings are used for vehicle guidance, then the system is simple to implement, but the reliability deteriorates when markings are obscured by soiling or wear
Solution Approach 1:
The patent introduces an intermediary object (the geometrically shaped target at the destination) that mediates between the vehicle's navigation system and the final destination. Instead of relying directly on degraded lane markings, the system uses this intermediate target as a more reliable reference point for determining arrival and positioning, thereby resolving the contradiction between implementation simplicity and guidance reliability.
Solution Approach 2:
The patent creates a simplified geometric representation (copy) of the destination as a target object with specific geometric properties. This geometric copy serves as a reliable reference that can be easily detected and measured, replacing the complex and potentially degraded real-world destination features, thus maintaining reliability while simplifying the guidance process.
2Measurement precision
If specific visual signatures are affixed at the destination, then the destination is uniquely identifiable, but the system complexity increases
Solution Approach 1:
The patent changes the parameters of the destination marker from complex visual signatures to simple geometric shapes with specific measurable properties (area, perimeter, shape factors). This parameter simplification maintains the ability to uniquely identify and precisely locate the destination while significantly reducing system complexity, as the geometric parameters are easier to detect and process.
Solution Approach 2:
The patent applies local quality by giving the destination a specific geometric characteristic (such as a particular area-to-perimeter ratio or shape factor) that distinguishes it from other objects. This localized geometric property enables unique identification of the destination without requiring complex visual signatures across the entire system, thereby reducing overall system complexity while maintaining identification precision.
3Reliability
If template matching is used for position detection, then reliability improves against obscuration, but the method requires pre-known signatures at the destination
Solution Approach 1:
The patent extracts only the essential geometric properties (area, perimeter, shape factors) of the destination target, removing the need for complex pre-known visual signatures. By taking out only the critical geometric parameters needed for reliable detection, the system achieves position detection reliability similar to template matching while simplifying the method by eliminating the requirement for storing and matching complex signature templates.
Data Source
AI summary
Many day-to-day driving situations require that an operator of a motor vehicle guide the motor vehicle along a specific course and bring the vehicle to a stop at a specific location, for example in a parking bay or at a loading platform. To assist a vehicle operator in such situations, a method and a suitable device for implementing this method, include detecting the potential target objects in the image data of an image sensor and identifying the potential target objects as potential destinations in a multi-stage exclusionary method, whereupon a trajectory describing an optimized travel path is computed at least in relation to the most proximate destination. By using the multi-stage exclusionary method according to the present invention, it is possible to reliably identify potential destinations in complex image scenarios solely on the basis of their geometric form, even when the destinations have not been encoded by specific symbols.