Frame Locked GPU Rasters for Real-Time Anti-Collision Processing
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Solution Overview
Problem
Current systems for real-time collision avoidance in vehicle control, such as Positive Train Control, face performance limitations due to slow data processing and high latency, which can lead to slower train speeds and longer distances between trains, especially when handling low-quality train location data and large numbers of passengers.
Innovation Solution
The implementation of a digital image processing system utilizing multiple GPU rasters in a frame-locked multi-raster environment, which processes object macrocells and employs a blitter for bit-block transfer functions, mimicking human perception and processing to achieve faster and more accurate collision avoidance calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If classic processors and software are used for collision avoidance calculations, then system complexity is lower, but processing speed is insufficient and reaction time exceeds requirements
Solution Approach 1:
The patent segments the image processing task into multiple independent rasters that can be processed in parallel. Each raster handles a specific portion of the scene, allowing simultaneous processing of multiple regions without requiring a single complex processor to handle everything sequentially. This segmentation enables the system to achieve high processing speeds while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent combines multiple GPU rasters into a unified processing system that works together to achieve high-speed collision avoidance calculations. By merging the capabilities of multiple rasters and using phase-locking to synchronize them, the system achieves processing speeds that exceed classic processors while managing complexity through coordinated parallel processing rather than a single monolithic processor.
2Productivity
If multiple GPU rasters are used for high-speed processing, then processing speed increases, but system complexity and coordination requirements increase
Solution Approach 1:
The patent employs periodic action through phase-locking multiple GPU rasters to operate in synchronized cycles. Each raster processes data in coordinated time periods, with phase-locking ensuring that all rasters complete their processing cycles in sync. This periodic coordination allows the system to achieve high throughput while managing complexity through regular, predictable operation patterns rather than chaotic parallel processing.
Solution Approach 2:
The system uses feedback mechanisms where each raster's processing status and results are continuously monitored and fed back to the coordination system. This feedback allows dynamic adjustment of processing parameters and ensures that all rasters remain synchronized, enabling high productivity while managing the complexity of coordinating multiple processing units through continuous state monitoring and adjustment.
3Loss of time
If processing latency is reduced for real-time control, then reaction time improves, but processing accuracy and validation opportunities decrease
Solution Approach 1:
The patent applies preliminary action by performing validation checks and safety verifications during the processing pipeline itself, rather than after processing is complete. The phase-locking mechanism and coordinated raster operations include built-in validation points that check processing accuracy while maintaining the overall fast processing timeline. This allows the system to reduce latency while preserving reliability through continuous validation throughout the processing sequence.
Data Source
AI summary
A system for controlling movement of a vehicle that includes a camera system mounted on the vehicle and configured to generate image signals of terrain within the vehicle path of movement of the vehicle, a radar system mounted on the vehicle and configured to generate distance signals to an object in the terrain, and a processor having multiple GPU rasters in a series data processing configuration that are configured to utilize a hypotenuse processing function for drawing lines from pixel to moving entity center (MEC) or from macro cell center to MEC, and a detector configured to test for raster frame lock between the multiple GPU rasters structured to determine a relative speed of the vehicle with respect to the object in the path of movement of the vehicle, and to generate control signals to alter the direction or speed of the vehicle or both to avoid the object.