Airport Autonomous Vehicle Sensing for 360° Obstacle Avoidance
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Solution Overview
Problem
Existing autonomous vehicles designed for public roadways are not suitable for airport operations due to the absence or difference in infrastructure, leading to inefficiencies and increased costs.
Innovation Solution
An autonomous vehicle system equipped with obstacle sensors, including 3D LiDAR and depth sensors, and a machine learning module, capable of navigating and avoiding obstacles within airports by generating a fused point cloud and predicting object trajectories to alter its path as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous vehicles designed for public roadways are used in airports, then automation is achieved, but reliability deteriorates due to absence of familiar infrastructure
Solution Approach 1:
The system changes the operational parameters by replacing infrastructure-dependent sensors (lane markings, traffic lights) with infrastructure-independent sensors (LiDAR, depth sensors, cameras) that can operate in airport environments without public roadway infrastructure
Solution Approach 2:
The patent introduces airport-specific infrastructure elements such as visual guidance markings, lighting systems, and communication protocols as intermediaries between the autonomous vehicle and the airport environment, enabling reliable operation without public roadway infrastructure
2Productivity
If manually operated vehicles are used in airports, then operational flexibility is maintained, but productivity decreases due to scheduling conflicts and human error
Solution Approach 1:
The autonomous vehicle performs self-navigation, self-monitoring, and self-correction using onboard sensors and processors, eliminating the need for manual operation while maintaining operational flexibility through autonomous decision-making algorithms
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor the environment, processors analyze data, and the vehicle adjusts its path in real-time, enabling autonomous operation that maintains flexibility while improving productivity
3Adaptability or versatility
If public roadway autonomous vehicle systems are deployed in airports, then cost reduction is expected, but adaptability deteriorates due to different environmental conditions
Solution Approach 1:
The sensor system is segmented into specialized modules (LiDAR for long-range detection, depth sensors for medium-range, cameras for visual recognition) that can independently process different types of environmental information, improving adaptability while managing complexity through modular architecture
Solution Approach 2:
The sensor suite is designed to perform multiple functions - obstacle detection, navigation, environmental mapping, and situation awareness - using a unified system architecture that reduces overall complexity while enhancing adaptability to various airport conditions
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
Enhances operational efficiency and safety by enabling autonomous navigation and task execution in airport environments, reducing human error and scheduling conflicts.
Implementation Method 1
The obstacle sensor is a planar LiDAR sensor
Implementation Method 2
The plurality of obstacle depth sensors includes a plurality of three-dimensional (3D) image sensors
Data Source
AI summary
Systems and methods provide an autonomous vehicle for operation in an airport. The autonomous vehicle includes a frame, a platform coupled to the frame and configured to support a load, a plurality of obstacle depth sensors positioned relative to the frame and together configured to detect obstacles 360 degrees about the frame, an obstacle planar sensor positioned relative to the frame and configured to detect obstacles in a horizontal plane about the frame, and an electronic processor coupled to the plurality of obstacle depth sensors and the obstacle planar sensor. The electronic processor is configured to operate the autonomous vehicle based on obstacles detected by the plurality of obstacle depth sensors and the obstacle planar sensor.