Deep Learning Vehicle Design Deviation Analysis
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Solution Overview
Problem
Designers face challenges in generating new vehicle designs that incorporate specific features, such as bulky sensor arrays for autonomous vehicles, while maintaining aesthetic consistency with existing vehicle fleets, as existing methods rely on subjective evaluation and are time-consuming.
Innovation Solution
A computer-implemented method using deep learning techniques to encode and decode vehicle designs within a latent space representation, allowing for the quantification of design similarity to a characteristic style and automatic generation of designs that align with the fleet's aesthetic, employing encoder and generator models trained with convolutional neural networks and generative adversarial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If designers generate numerous vehicle designs to accommodate specific features like bulky sensor arrays, then the vehicle can include necessary functional features, but the time required for subjective evaluation increases significantly
Solution Approach 1:
The patent replaces the manual, subjective mechanical evaluation process with an automated computer-based system using deep learning models. The encoder-generator architecture automatically evaluates design adherence to characteristic styles, eliminating the need for time-consuming human review of numerous design iterations while maintaining the ability to assess both functional features and aesthetic consistency.
Solution Approach 2:
The system enables self-service evaluation where the computer-based model autonomously assesses vehicle designs against characteristic styles without requiring human intervention. The encoder-generator framework automatically processes designs, computes adherence metrics, and generates feedback, allowing designers to rapidly evaluate multiple configurations independently.
2Ease of operation
If designers rely on subjective evaluation based on experience and intuition, then aesthetic judgment can be made, but the evaluation cannot be performed in an objective and deterministic manner
Solution Approach 1:
The patent implements an automated feedback mechanism where the encoder-generator model provides quantitative adherence metrics to designers. The system encodes designs into latent space representations, compares them against characteristic style embeddings, and generates deterministic feedback scores that objectively measure how well designs conform to desired aesthetic qualities, replacing subjective intuition with measurable data.
Solution Approach 2:
The system transforms subjective aesthetic evaluation into objective parameter-based measurement by mapping designs and styles into a shared latent space. The adherence metric is computed as a quantitative parameter based on the distance or similarity between design embeddings and style embeddings, converting qualitative aesthetic judgment into precise, deterministic numerical evaluation.
3Reliability
If bulky sensor arrays are added to autonomous vehicles for accurate environment mapping, then navigation functionality is improved, but the vehicle style becomes awkward and diverges from fleet characteristics
Solution Approach 1:
The patent separates the evaluation of functional requirements from aesthetic qualities by processing them through distinct but integrated pathways in the encoder-generator framework. The system can independently assess the presence and configuration of sensor arrays while separately evaluating adherence to characteristic styles, allowing designers to optimize both functionality and aesthetics without compromise.
Solution Approach 2:
The patent introduces a latent space dimension that simultaneously captures both functional and aesthetic properties of vehicle designs. By embedding designs and styles in a high-dimensional latent space, the system can measure adherence across multiple dimensions including sensor configuration and aesthetic consistency, enabling holistic evaluation that reconciles functional requirements with stylistic harmony.
Data Source
AI summary
A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.