Vehicle Sensor Feature Alignment Across Different Sensor Setups
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Solution Overview
Problem
Existing vehicle sensor systems face challenges in adapting to different sensor arrangements due to variability in sensor setups, including differences in number, type, and placement, leading to inefficiencies in model deployment and increased complexity, which affects the accuracy of vehicle control instructions.
Innovation Solution
A transformation technique using a variational autoencoder model aligns top-down features from different sensor arrangements, allowing for seamless adaptation across various sensor setups by minimizing KL-divergence in latent space feature distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensor data from different sensor arrangements is used directly without alignment, then device complexity is reduced, but measurement precision and reliability of vehicle control deteriorate
Solution Approach 1:
The patent introduces an intermediary alignment process that transforms sensor data from different sensor arrangements into a common reference frame. This mediator (the alignment transformation) enables compatibility between diverse sensor setups without requiring complete retraining of perception models, thus reducing complexity while maintaining accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming sensor data parameters (position, orientation, scale) to align with a reference sensor arrangement. This allows data from different sensor configurations to be integrated accurately without changing the underlying perception models, resolving the contradiction between simplicity and precision.
2Measurement precision
If sensor data from different sensor arrangements is aligned through transformation, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary alignment transformations on sensor data before feeding it into perception models. By pre-aligning the data in advance, the system avoids the need for complex real-time alignment during inference, reducing overall system complexity while maintaining high measurement precision.
3Measurement precision
If perception models are retrained for each sensor arrangement, then measurement precision is maintained, but loss of time and productivity decrease
Solution Approach 1:
The patent creates a transformed copy of sensor data that matches the reference sensor arrangement format. Instead of retraining models for each sensor type, the system copies and transforms the input data to match the training distribution, preserving model accuracy while eliminating time-consuming retraining processes.
4Adaptability or versatility
If alignment transformation is applied to sensor data, then adaptability between sensor arrangements improves, but processing time and computational resources increase
Solution Approach 1:
The patent creates a universal alignment framework that can handle multiple sensor arrangements using a single reference model. This multi-functional approach enables the system to adapt to various sensor configurations through a unified transformation process, improving versatility while managing computational overhead through efficient transformation algorithms.
Data Source
AI summary
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. A method is disclosed for aligning top-down features from two sensor arrangements and generating vehicle control instructions. The method includes receiving first sensor data from a first sensor arrangement and second sensor data from a second sensor arrangement. The method further includes determining a first set of top-down and a second set of top-down features based on the sensor data. A transformation is determined based on the first set of top-down features and the second set of top-down features to align the second set of top-down features with the first set of top-down features. Finally, vehicle control instructions for a vehicle are determined based on the transformation. Other aspects and features are also claimed and described.