Driving Scene Embeddings for Realistic AV Simulation Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional simulation systems for autonomous vehicles generate a high number of useless and infeasible driving scenes due to random scene attribute generation, leading to inefficient training of prediction models.
Innovation Solution
Utilizing a generative machine-learning network, such as a Generative Adversarial Network (GAN) with an auto-encoder, to process real-world sensor data into feature embeddings that represent driving scene features, which are stored as unique character profiles for generating realistic simulated environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random scene attribute generation is used, then simulation generation is simple and fast, but the number of useless and infeasible driving scenes increases
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to copy real-world driving scenes and create synthetic training data. The generator network creates fake driving scenes that mimic real scene characteristics, while the discriminator network evaluates their authenticity. This copying approach generates high-quality synthetic data that reflects real driving conditions without requiring actual real-world recordings, thus improving simulation quality while maintaining generation efficiency.
Solution Approach 2:
The patent transforms real driving scene parameters into a compressed latent space representation using an autoencoder, then generates new scenes by sampling from this latent space. The real scene parameters (images, depth maps, segmentation masks) are encoded into a lower-dimensional latent vector, and new scenes are synthesized by decoding latent vectors sampled from a Gaussian distribution. This parameter transformation enables efficient generation of diverse and realistic driving scenes.
2Reliability
If real-world sensor data is processed through GANs, then simulation realism is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex GAN training process into separate functional components: a generator network that synthesizes scenes, a discriminator network that validates authenticity, and an autoencoder that compresses real scene data into latent representations. Each component can be trained and optimized independently, reducing the overall computational burden while maintaining high simulation realism through the collaborative interaction of these segmented modules.
3Adaptability or versatility
If feature embedding vectors are stored as character profiles, then simulation diversity is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts the essential features of real driving scenes into compressed latent vectors through the autoencoder. Instead of storing complete real scene data, only the compressed latent representations are stored as character profiles in the database. This extraction approach maintains the essential diversity and characteristics of real driving scenes while dramatically reducing storage requirements, as the latent vectors are much smaller than the original image data.
Data Source
AI summary
System, methods, and computer-readable media for processing real-world environments into feature vectors that can be randomly selected into a synthetic scene. Scene data is provided to a generative machine-learning network that processes road types, environment lighting conditions, object behaviors, and AV trajectories of an AV driving scene from the sensor data into feature embeddings that represent such features from the driving scenes. The feature embeddings may be stored as unique character profiles in a scene database to be randomly selected into the synthetic scene.