Multi-Sensor Simulation of LIDAR Data for Autonomous Guidance
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Solution Overview
Problem
Autonomous control systems for vehicles face challenges in achieving safe and efficient navigation without high-capacity sensors, which are costly and complex, while lower-capacity sensors are more affordable but provide limited and fragmented data.
Innovation Solution
The system simulates high-capacity sensor data using a combination of lower-capacity sensors and neural networks, allowing vehicles to perform autonomous guidance by synthesizing sensor data from multiple sources, including cameras and RADAR sensors, to mimic the precision of high-capacity LIDAR sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-capacity sensors (e.g., LIDAR) are used, then measurement precision and field-of-view are improved, but device complexity and cost increase
Solution Approach 1:
The patent combines data from multiple lower-capacity sensors (cameras, RADAR) to simulate and reconstruct the measurement capabilities of high-capacity LIDAR sensors. By merging information from these diverse sensor sources, the system achieves comprehensive environmental perception without requiring expensive LIDAR hardware.
Solution Approach 2:
The system creates a simulated copy of high-capacity LIDAR sensor data by processing and fusing inputs from lower-capacity sensors. This simulated LIDAR data replicates the functional output of actual LIDAR sensors, allowing existing detection and control systems to operate as if they received genuine high-capacity sensor inputs.
2Measurement precision
If high-capacity sensors are used, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive, complex high-capacity sensors with multiple inexpensive, readily available lower-capacity sensors. While individual low-capacity sensors have limited capabilities, their collective data fusion produces precision comparable to high-capacity sensors at a fraction of the cost.
Solution Approach 2:
The system uses universal, commercially available sensors (cameras, RADAR) that can be deployed across multiple vehicle platforms without specialized manufacturing. This multi-functional approach allows the same sensor suite to serve various autonomous vehicle applications while maintaining cost-effectiveness.
3Device complexity
If lower-capacity sensors are used, then device complexity and cost are reduced, but measurement precision and data completeness deteriorate
Solution Approach 1:
The patent introduces data fusion algorithms and neural network processing as intermediary layers between the lower-capacity sensors and the autonomous control system. These intermediaries process, correlate, and synthesize sensor inputs to compensate for individual sensor limitations, achieving precision equivalent to high-capacity sensors.
Solution Approach 2:
The system compensates for limited field-of-view and measurement precision of individual low-capacity sensors by adding temporal and spatial dimensions through data fusion. By combining data from multiple sensors positioned at different locations and processing it across time, the system reconstructs a complete, high-precision environmental model.
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.