Automotive Lighting Image Compression With Deep Autoencoders
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Solution Overview
Problem
Current automotive lighting systems face inefficiencies in data management due to the high number of light sources, which requires significant bandwidth for control, and existing compression methods result in data loss that does not meet automotive regulatory standards.
Innovation Solution
A method utilizing a deep autoencoder with encoder and decoder blocks is implemented to process and compress image data for automotive lighting, allowing for flexible data loss and improved compression rates, reducing the need for high bandwidth communication by training the autoencoder to minimize data loss and optimize compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current compression methods are used to reduce data bandwidth, then bandwidth consumption is reduced, but data loss increases which may not meet automotive regulations
Solution Approach 1:
The patent transforms the data representation parameters by converting image data into a compressed latent space representation through a trained autoencoder model. This parameter transformation enables efficient bandwidth utilization while preserving essential light pattern information through optimized encoding that adapts to regulatory requirements.
Solution Approach 2:
The patent replaces traditional mechanical compression methods with an intelligent neural network-based autoencoder system. This substitution enables adaptive compression that learns optimal representations of light patterns, achieving better compression ratios without unacceptable data loss compared to conventional compression algorithms.
2Adaptability or versatility
If the number of light sources is increased to provide adaptive lighting functionalities, then lighting functionality is improved, but data management complexity and bandwidth requirements increase
Solution Approach 1:
The patent merges the control of multiple individual light sources into a unified image data representation that captures the overall light pattern. This consolidation reduces data management complexity by treating multiple light sources as a single visual pattern rather than managing each source separately, thereby simplifying communication protocols.
Solution Approach 2:
The patent uses the autoencoder to create a compressed copy or representation of the light pattern data. Instead of transmitting full-resolution data from multiple light sources, the system transmits a compressed encoded version that preserves essential pattern information while significantly reducing data volume and management complexity.
3Reliability
If the CAN protocol bandwidth is limited as decided by car manufacturers, then system safety and reliability are improved, but data transmission capability for lighting control deteriorates
Solution Approach 1:
The patent changes the data transmission parameters by encoding light pattern information into a compressed format that fits within limited CAN protocol bandwidth. The autoencoder learns to prioritize essential pattern features, enabling reliable transmission under bandwidth constraints while maintaining sufficient information for accurate light pattern reproduction.
Solution Approach 2:
The patent implements a dynamic compression approach where the autoencoder adapts its encoding to prioritize critical light pattern information. This dynamic prioritization ensures that essential data for safety and functionality is transmitted reliably within the limited bandwidth, while less critical details are compressed or omitted based on regulatory requirements.
Data Source
AI summary
An automotive lighting arrangement and a method for manufacturing an automotive lighting arrangement. The method includes training a deep autoencoder to process image data, installing the encoder block in an automotive control unit of an automotive vehicle and installing the decoder block in an automotive lighting module of the automotive vehicle.