Simulation Data Generation for Autonomous Driving
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Solution Overview
Problem
Current systems for training autonomous driving systems face challenges in generating photo-realistic simulation data, particularly in simulating realistic vehicle and pedestrian behaviors, which are crucial for effective training and testing.
Innovation Solution
A method and system that analyze real input data to identify environment and agent classes, generating simulated driving environments with realistic agent behaviors and modifying model parameters to minimize the difference between simulated and real statistical fingerprints, using image refining models to enhance realism and content preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a rendering engine generates synthetic images for simulation, then the simulation data can be produced efficiently, but the images do not appear photo-realistic due to issues with color saturation, gradients, and lighting
Solution Approach 1:
An image refining model is introduced as an intermediary between the rendering engine and the final simulation output. The rendering engine generates base synthetic images efficiently, and the image refining model refines them to achieve photo-realistic quality, thus resolving the contradiction between efficiency and realism
Solution Approach 2:
The patent replaces traditional manual image editing or complex physics-based rendering with a machine learning-based image refining model. This substitution enables automated refinement of synthetic images to achieve photo-realistic quality without sacrificing generation efficiency
2Loss of time
If simulated environments are used for training autonomous driving systems, then training costs and time are reduced, but the simulation data lacks realistic behavior patterns of agents
Solution Approach 1:
The system uses statistical fingerprints derived from real driving data as feedback to guide the simulation generation process. By comparing simulation statistics against real-world statistics and iteratively adjusting the simulation parameters, the system achieves realistic agent behavior while maintaining efficient simulated training
Solution Approach 2:
The patent adjusts simulation parameters such as agent behavior patterns, environmental conditions, and traffic flow characteristics to match real-world statistical distributions. This parameter tuning enables the simulated environment to reproduce realistic behavior patterns while keeping training efficient
3Manufacturing precision
If image refining models are applied to enhance synthetic images, then photo-realistic quality is achieved, but the processing complexity and computational resources increase
Solution Approach 1:
The rendering engine generates pre-processed synthetic images with proper base structures, lighting frameworks, and geometric accuracy before refinement. This preliminary preparation reduces the complexity of the subsequent image refining process by focusing computational resources on specific realism enhancements rather than complete image generation
Data Source
AI summary
A method for training a model for generating simulation data for training an autonomous driving agent, comprising: analyzing real data, collected from a driving environment, to identify a plurality of environment classes, a plurality of moving agent classes, and a plurality of movement pattern classes; generating a training environment, according to one environment class; and in at least one training iteration: generating, by a simulation generation model, a simulated driving environment according to the training environment and according to a plurality of generated training agents, each associated with one of the plurality of agent classes and one of the plurality of movement pattern classes; collecting simulated driving data from the simulated environment; and modifying at least one model parameter of the simulation generation model to minimize a difference between a simulation statistical fingerprint, computed using the simulated driving data, and a real statistical fingerprint, computed using the real data.