Multi-Camera Image Backbones for Accuracy-Speed Tradeoffs
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Solution Overview
Problem
Autonomous vehicles face computational challenges in processing vast amounts of sensor data from multiple cameras, leading to inefficiencies in resource usage and potential blind spots due to the need to maintain high accuracy and precision across all camera views.
Innovation Solution
Implementing two backbone models: a high precision but slower model for high accuracy and a high speed but less accurate model, dynamically selecting which model to use based on vehicle behavior and camera importance, combining outputs for unified representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high precision imaging backbone is used for all cameras, then measurement precision is improved, but use of energy increases and productivity decreases
Solution Approach 1:
The patent segments the imaging backbone into two distinct models: a high precision imaging backbone for accurate feature detection and a high speed imaging backbone for efficient processing. This segmentation allows the system to allocate different processing capabilities to different cameras based on their importance and the current driving scenario, rather than uniformly applying high precision processing to all cameras.
Solution Approach 2:
The patent applies local quality by assigning different precision levels to different cameras based on their importance. Critical cameras (e.g., front-facing cameras for collision detection) use the high precision imaging backbone, while less critical cameras use the high speed imaging backbone. This ensures that computational resources are concentrated where they are most needed for safety-critical functions.
2Measurement precision
If high precision imaging backbone is used for all cameras, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The imaging backbone is segmented into high precision and high speed models, allowing parallel processing where critical cameras are processed with high precision while non-critical cameras are processed with high speed models, maintaining overall system productivity.
Solution Approach 2:
Different precision levels are applied locally to different cameras based on their importance. This ensures that processing speed is optimized for the overall system while maintaining high accuracy where it matters most for safety-critical detection tasks.
3Use of energy by moving object
If computational resources are reduced, then use of energy decreases, but reliability worsens due to potential blind spots
Solution Approach 1:
The patent ensures reliability by assigning high precision processing to critical cameras that cover essential viewing angles for safety. This localized high precision processing maintains comprehensive surroundings imaging capability while reducing overall computational resource consumption for less critical cameras.
Solution Approach 2:
The system dynamically selects which imaging backbone to use for each camera based on the current driving scenario and camera importance. This dynamic adaptation allows the system to maintain reliability in critical situations while optimizing energy consumption during normal operation.
Data Source
AI summary
Systems and methods for a multi-camera object detector having two or more backbone models. In particular, systems and methods are provided for including two or more backbone machine learning models, with one backbone optimized for speed and the other backbone optimized for precision. In particular, the first backbone can be highly accurate but slower than the second backbone and with a higher computer resource usage. The second backbone can be fast and efficient but have lower accuracy for object detection. In some examples, the second backbone can use fewer images and/or lower resolution images. The determination of which backbone to use can be based on fixed rules, or it can be determined based on another machine learning component. The outputs from the first and second backbones for each camera can be combined together into a unified representation, such as a bird's eye view (BEV) space.