Multi-Sensor Simulation for High-Capacity Autonomous Perception
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Solution Overview
Problem
Autonomous control systems for vehicles face challenges in achieving accurate guidance without high-capacity sensors, which are costly and complex, while lower-capacity sensors provide fragmented and less precise data.
Innovation Solution
Simulating high-capacity sensor data using a combination of lower-capacity sensors through neural networks, synthesizing sensor data to achieve comparable accuracy and precision for vehicle guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-capacity sensors are used, then measurement precision and information quality improve, but device cost and complexity increase
Solution Approach 1:
The patent creates a virtual copy of high-capacity sensor data by training a neural network to map inputs from multiple lower-capacity sensors to the expected output of a high-capacity sensor. This synthetic copy enables downstream systems designed for high-capacity sensors to function with cheaper sensor configurations.
Solution Approach 2:
The patent replaces expensive, complex high-capacity sensors with multiple inexpensive lower-capacity sensors. While individual low-capacity sensors have limitations, their combined data processed through neural networks achieves comparable effectiveness to high-capacity sensors at fraction of the cost.
2Loss of information
If high-capacity sensors are used, then information quality improves, but sensor cost increases
Solution Approach 1:
The patent merges data from multiple lower-capacity sensors to reconstruct environmental information that rivals or exceeds the quality of single high-capacity sensors. By combining inputs from several inexpensive sensors positioned differently, the system achieves comprehensive environmental awareness at lower total cost.
Solution Approach 2:
The neural network learns to generate synthetic high-capacity sensor data that copies the information quality of expensive sensors while using affordable sensor inputs, effectively decoupling information quality from sensor cost.
3Ease of manufacture
If multiple lower-capacity sensors are combined, then sensor cost decreases, but data integration complexity increases
Solution Approach 1:
The patent introduces a neural network as an intermediary processing layer that automatically handles the complexity of fusing data from multiple lower-capacity sensors. This intermediary learns optimal integration strategies during training, transforming multiple imperfect inputs into unified high-quality output without requiring manual feature engineering or complex fusion algorithms.
Data Source
AI summary
An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.