Dynamic Image Compression for Autonomous Vehicle Cameras
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Solution Overview
Problem
Autonomous vehicles face challenges in processing high-resolution images from multiple cameras, leading to increased data-processing loads that can exceed the computational capacity of the Electronic Control Unit (ECU), potentially compromising response times to dynamic events.
Innovation Solution
A dynamic image compression system that adjusts image compression techniques on a per-camera basis, prioritizing higher compression ratios in regions of lower priority and lower compression ratios in regions of higher priority to balance processing time and information loss, ensuring satisfactory performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are captured to achieve LiDAR-level performance, then object detection capability is improved, but data-processing load increases beyond ECU computational capacity
Solution Approach 1:
The patent applies different image compression techniques to different cameras based on their priority levels. High-priority cameras (those capturing critical driving scenes) use compression techniques with less information loss, while low-priority cameras use techniques with higher compression ratios. This local differentiation resolves the contradiction by ensuring sufficient image quality where needed while reducing overall processing load.
Solution Approach 2:
The system dynamically adjusts compression parameters based on scene priority and computational resource availability. By changing compression ratio, compression technique type, and other parameters adaptively, the system maintains object detection capability when needed while reducing processing load during high-computational-demand situations.
2Productivity
If image compression techniques with higher compression ratios are used, then processing time is reduced, but information loss increases
Solution Approach 1:
Different compression techniques with varying information loss characteristics are applied to different cameras based on their priority. High-priority cameras receive compression treatment that minimizes information loss, while low-priority cameras undergo more aggressive compression. This resolves the contradiction by localizing the impact of compression-induced information loss to non-critical regions.
Solution Approach 2:
The system applies compression selectively rather than uniformly across all cameras. By applying full compression only where necessary (low-priority cameras) and minimal compression where critical (high-priority cameras), the system achieves sufficient processing speed while preserving necessary information.
3Device complexity
If uniform image compression is applied to all cameras, then system complexity is reduced, but performance in critical regions deteriorates
Solution Approach 1:
The patent implements a priority-based camera classification system where cameras are assigned different compression techniques based on their importance to autonomous driving tasks. This local differentiation ensures that critical cameras maintain high image quality while non-critical cameras undergo aggressive compression, resolving the contradiction between system simplicity and performance reliability.
Solution Approach 2:
The camera array is segmented into priority groups (high-priority and low-priority cameras) based on their operational importance. Each segment receives appropriate compression treatment, with high-priority cameras using techniques that preserve critical information and low-priority cameras using techniques that maximize compression. This segmentation resolves the contradiction by organizing the system into manageable groups with differentiated requirements.
Data Source
AI summary
A system for dynamic image compression in autonomous vehicles with multiple cameras. The cameras of a vehicle may be prioritized based on various features, and different image compression techniques may be assigned to different cameras in the vehicle, based on the priorities of the cameras, to reduce overall processing load of the vehicle.