Sensor Imaging Tensor for Adaptive Autonomous Vehicle Perception
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Solution Overview
Problem
Current AI-based perception systems for autonomous driving heavily rely on neural networks trained on specific sensor configurations, limiting their generalizability to different sensor setups and requiring retraining with every hardware change, which is time-consuming and resource-intensive.
Innovation Solution
Incorporating a Sensor Imaging Tensor (SIT) that encodes sensor metadata such as exposure, gain, and lens properties into the learning process, allowing the system to adapt to various sensor configurations without full retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained on specific sensor configurations, then the system achieves high accuracy for that specific setup, but the system cannot generalize to different sensor setups and requires retraining from scratch
Solution Approach 1:
The patent applies parameter changes by encoding sensor configuration parameters (exposure, gain, focus, dynamic range, bit depth) as additional input channels to the neural network. This allows the network to receive explicit information about sensor settings and adjust its processing accordingly, enabling generalization across different sensor configurations without retraining. The network learns to interpret sensor data conditioned on these encoded parameters rather than being tied to a specific sensor setup.
Solution Approach 2:
The patent segments the sensor information into distinct encoded parameters (exposure, gain, focus, dynamic range, bit depth) that are injected into the network as separate channels. This segmentation allows the network to process each sensor parameter independently and learn their individual effects on the data, improving adaptability to different sensor configurations while maintaining accurate perception.
2Adaptability or versatility
If the entire network is retrained from scratch for every sensor hardware change, then the system adapts to the new configuration, but significant time and computational resources are wasted
Solution Approach 1:
The patent applies preliminary action by pre-encoding sensor configuration parameters into the input data structure before the network processes the main sensor data. This preparation step allows the network to adapt to new sensor hardware through inference rather than requiring retraining, as the sensor parameters are already embedded in the input tensor. The network learns general patterns from diverse training data that includes varied sensor configurations, enabling quick adaptation to new hardware.
Solution Approach 2:
The encoded sensor parameters act as an intermediary that mediates between the physical sensor hardware and the neural network. Instead of the network directly processing raw sensor data from unknown configurations, the encoded parameters provide contextual information about the sensor setup, allowing the network to adjust its processing without retraining. This intermediary layer enables seamless adaptation to new sensor hardware.
3Adaptability or versatility
If the entire network is retrained from scratch for every sensor hardware change, then the system adapts to the new configuration, but substantial computer resources and data are consumed
Solution Approach 1:
The patent reduces computational resource consumption by changing the approach from retraining network parameters to providing parameter information as input. By encoding sensor configuration parameters (exposure, gain, focus, dynamic range, bit depth) into the input tensor, the system enables the existing trained network to adapt to new sensor hardware without requiring resource-intensive retraining processes.
Solution Approach 2:
The system applies self-service by enabling the neural network to automatically adapt to new sensor configurations through the encoded parameter inputs without requiring external retraining interventions. The network uses the sensor parameter information to self-adjust its processing, eliminating the need for resource-consuming retraining cycles and reducing dependency on extensive annotated training datasets for each new sensor setup.
Data Source
AI summary
A method for processing image data and sensor data in an autonomous vehicle includes receiving image data and sensor data generated by one or more sensors of an autonomous vehicle, encoding the sensor data into a multichannel sensor imaging tensor to generate encoded sensor data, providing the image data and the encoded sensor data to an autonomous driving system trained to control the autonomous vehicle, and executing, by the autonomous driving system, one or more operations for controlling the autonomous vehicle based at least in part on the image data and the encoded sensor data.