Synthetic LIDAR Conversion for Camera-Based Vehicle Control
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Solution Overview
Problem
The high cost of LIDAR sensors in autonomous vehicles and the challenge of integrating LIDAR-based computing systems with vision-based systems without increasing production costs.
Innovation Solution
Retrofitting a LIDAR-based vehicle computing system to operate with vision-based sensor data by converting image data into synthetic LIDAR data using a generative adversarial network (GAN) model, simulating LIDAR sensor characteristics, and integrating it with the existing LIDAR-based processing stack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used in autonomous vehicles, then measurement precision and reliability are improved, but device cost increases
Solution Approach 1:
The patent generates synthetic LIDAR data by copying the functional characteristics of LIDAR sensor outputs through vision-based sensors. A machine learning model transforms camera images into synthetic point cloud data that mimics real LIDAR measurements, enabling the vehicle controller to operate with vision-based sensors while maintaining compatibility with LIDAR-based processing stacks without requiring actual LIDAR hardware
Solution Approach 2:
The patent replaces expensive LIDAR sensors with cheaper vision-based sensors (cameras). The machine learning model compensates for the lower cost sensor's limitations by generating synthetic LIDAR data that provides the necessary measurement precision at a fraction of the cost of actual LIDAR hardware
2Adaptability or versatility
If LIDAR-based computing systems are integrated with vision-based systems, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal processing solution where a single LIDAR-based vehicle controller can process both real LIDAR data and synthetic LIDAR data generated from vision-based sensors. The machine learning model acts as a universal translator that adapts vision-based sensor inputs to the LIDAR data format expected by the existing processing stack, eliminating the need for separate processing paths
Solution Approach 2:
The machine learning model serves as an intermediary component between vision-based sensors and the LIDAR-based processing stack. It transforms camera images into synthetic LIDAR point clouds, mediating the interface between incompatible sensor modalities and enabling seamless integration without requiring modifications to the existing LIDAR-based controller
3Adaptability or versatility
If multiple processing stacks are maintained for different sensor modalities, then adaptability is improved, but maintenance difficulty increases
Solution Approach 1:
The patent merges multiple processing stacks into a single unified system. By generating synthetic LIDAR data from vision-based sensors, the system combines the advantages of both LIDAR and vision-based processing into one integrated pipeline, eliminating the need to maintain separate processing stacks for different sensor modalities and significantly reducing maintenance complexity
Data Source
AI summary
Systems and methods for retrofitting a light detection and ranging (LIDAR)-based vehicle computing system to operate with vision-based sensor data are provided. For example, a method implemented by a vehicle may include receiving, from one or more sensors of a first sensing modality at the vehicle, first sensor data associated with a surrounding environment of the vehicle; and retrofitting a vehicle controller of the vehicle that is based on a second sensing modality different from the first sensing modality to operate on the first sensor data, where the retrofitting includes generating second sensor data from the first sensor data based on the second sensing modality; and determining, by the vehicle controller, an action for the vehicle based at least in part on the generated second sensor data.