Monocular CNN Speed Estimation via Synchrony Autoencoder
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI systems for estimating vehicular motion rely on costly and complex hardware, such as dual cameras and powerful GPUs, which are not commercially viable due to high computational burdens, power consumption, and design challenges in automotive environments, especially when implementing Convolutional Neural Networks (CNNs) for real-time processing.
Innovation Solution
A system that estimates vehicular motion using monocular video data by pre-training a convolutional neural network with synchrony autoencoder filters, allowing for accurate speed estimation on less powerful hardware like the Jetson TX1 or Snapdragon processors, reducing computational requirements and power consumption while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual camera/stereo estimation techniques are used, then motion estimation accuracy is improved, but hardware cost and complexity increase
Solution Approach 1:
The patent extracts and utilizes temporal information from monocular video sequences, removing the need for stereo depth information. By focusing on optical flow and temporal consistency, the system achieves motion estimation without the complex dual-camera hardware, directly resolving the contradiction between accuracy and hardware complexity
Solution Approach 2:
The patent creates a virtual depth map through optical flow computation from monocular video, effectively copying the depth estimation function normally requiring stereo cameras. This virtual depth representation enables accurate motion estimation while maintaining simple hardware configuration
2Measurement precision
If Convolutional Neural Networks are used for real-time processing, then motion estimation accuracy is improved, but computational burden and power consumption increase
Solution Approach 1:
The patent performs preliminary processing by computing optical flow fields and temporal consistency maps before feeding data to the CNN. This preprocessing extracts key motion features, reducing the computational burden on the neural network and enabling deployment on power-constrained automotive systems while maintaining accuracy
Solution Approach 2:
The patent segments the motion estimation task into distinct stages: optical flow computation, temporal consistency analysis, and CNN-based refinement. This segmentation allows each component to be optimized independently, reducing overall computational requirements and power consumption while preserving measurement precision
3Measurement precision
If large amount of training data is used for CNN, then motion estimation accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The patent generates synthetic training data through rendering virtual scenes with known ground truth motion, performing preliminary data preparation offline. This approach creates large-scale training datasets without requiring extensive real-world data collection and annotation, reducing training time while maintaining accuracy through diverse synthetic scenarios
Solution Approach 2:
The patent uses synthetic data generation to copy realistic driving scenarios in virtual environments, creating training datasets that replicate real-world conditions without the time and resource costs of actual data collection. This copying approach enables efficient training while preserving measurement precision through controlled ground truth labels
4Productivity
If powerful GPUs are installed in vehicle, then real-time processing capability is improved, but cooling requirements and weight increase
Solution Approach 1:
The patent employs lightweight neural network architectures and optimized algorithms that can run on low-power, low-weight processors. By designing computationally efficient models that require minimal processing power, the system achieves real-time capability without heavy GPU hardware, directly reducing vehicle weight and cooling requirements
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A vehicle movement parameter, such as ego-speed, is estimated using real-time images captured by a single camera. The captured images may be analyzed by a pre-trained convolutional neural network to estimate vehicle movement based on monocular video data. The convolutional neural network may be pre-trained using filters from a synchrony autoencoder that were trained using unlabeled video data captured by the vehicle's camera while the vehicle was in motion. A parameter corresponding to the estimated vehicle movement may be output to the driver or to a driver assistance system for use in controlling the vehicle.