Multi-Sensor Fusion for Accurate Automated Vehicle Taxiing
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Solution Overview
Problem
The challenge of automating complex and potentially dangerous taxiing operations for vehicles, such as aircraft, is hindered by the inefficiencies and inaccuracies of using multiple sensors, which can lead to errors in object detection and tracking, especially under varying environmental conditions.
Innovation Solution
A multi-sensor fusion system that combines data from different sensors, such as LiDAR and optical sensors, to create a unified detection report, allowing a computing device to automate taxiing operations by accurately identifying and tracking objects, surfaces, and obstacles, and generating optimized routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used to gather environmental information, then the accuracy and safety of automation activities are improved, but the complexity of processing and rendering unified environmental view deteriorates
Solution Approach 1:
The patent merges data from multiple sensors (lidar, optical sensors, radar) into a unified environmental representation. The system combines sensor inputs through data fusion algorithms to create a cohesive view of the environment, enabling accurate object detection and tracking while managing the complexity through integrated processing architecture.
Solution Approach 2:
The computing device performs multiple functions including processing different sensor types, fusing their data, detecting objects, tracking them over time, and generating automation commands. This multi-functional approach consolidates complexity into a single system that handles diverse sensor inputs and produces unified outputs for automation control.
2Measurement precision
If multiple sensors are used to detect environmental information, then the accuracy of object detection is improved, but the difficulty of identifying errors and processing different sensor types increases
Solution Approach 1:
The system incorporates feedback mechanisms where detected objects and their trajectories are continuously verified against multiple sensor inputs. When discrepancies arise between sensors, the system identifies and resolves errors through cross-validation, maintaining high detection accuracy while managing the complexity of error identification through systematic verification processes.
Data Source
AI summary
An object classification system for a vehicle detects, with a computing device, a first field of view with a first sensor and a second field of view with a second sensor, each sensor mounted on the vehicle. The computing device may fuse the first field of view with the second field of view to form a unified detection report, which may be used to automate portions of a taxiing operation in response to information in the unified detection report.