Vehicle Guidance Using Predicted Motion for Collision Avoidance
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Solution Overview
Problem
Current systems for vehicle guidance, particularly for unmanned aerial vehicles, are limited by resource costs, mainly in processing and storage, which restrict the detection of moving objects and the processing of depth information, leading to inefficiencies in navigation and collision avoidance.
Innovation Solution
A system comprising an image sensor, a motion and orientation sensor, and hardware-implemented processors that detect objects, predict vehicle paths, and determine potential intersections, using predicted motion and imaging components to assess the likelihood of collisions and adjust vehicle behavior accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If processing and storage resources are increased to detect moving objects and process depth information, then detection capability and navigation safety are improved, but resource costs and system complexity increase
Solution Approach 1:
The system segments the environment into multiple depth layers (near, mid, far ranges) and processes objects at different depths separately. This segmentation allows the system to focus computational resources on relevant depth zones rather than processing all spatial information uniformly, improving detection reliability while managing system complexity through hierarchical organization of processing tasks.
Solution Approach 2:
The system performs preliminary depth estimation and object classification before full collision avoidance processing. By pre-processing depth information and identifying potential collision risks in advance, the system prepares critical data structures and predictions that reduce the computational burden during real-time navigation decisions, thereby improving safety without proportionally increasing overall system complexity.
2Measurement precision
If processing resources are increased to detect moving objects, then detection accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The system applies partial processing by focusing computational effort only on depth regions and objects that are relevant to potential collisions. Rather than analyzing all detected objects with equal detail, the system selectively processes objects within critical depth zones and those presenting collision risks, achieving sufficient detection accuracy for safety-critical applications while reducing overall processing time and energy consumption.
Solution Approach 2:
The system applies different processing qualities to different spatial regions. High-precision processing is applied to near-field objects and critical depth zones where collision risks are highest, while lower-precision processing is used for distant objects. This local differentiation of processing quality maintains detection accuracy for critical targets while reducing computational burden for less critical areas.
3Reliability
If depth information processing is enhanced for collision avoidance, then navigation safety is improved, but computational load and processing complexity increase
Solution Approach 1:
The depth information processing is segmented into distinct computational stages: depth estimation from multiple sensors, depth map generation, object detection in depth space, and collision risk assessment. Each stage processes specific aspects of depth information independently, allowing parallel processing and optimizing computational efficiency while maintaining comprehensive collision avoidance capability.
Solution Approach 2:
The system performs preliminary depth map generation and object candidate identification before executing full collision avoidance algorithms. By pre-computing depth structures and identifying potential collision targets in advance, the system reduces the computational load during critical real-time decision-making, improving both safety and processing efficiency.
Data Source
AI summary
A vehicle includes systems and methods to guide the vehicle. A sensor detects objects around the vehicle and processor includes a predicted motion component and a predicted imaging component to avoid objects around the vehicle. The system and method then avoid the object if the processor determines that an intersection will occur between the object and the vehicle.