Procedural Road Mesh Generation for Scalable AV Simulation
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Solution Overview
Problem
Existing techniques for generating simulated environments for training and testing autonomous vehicles are computationally intensive, time-consuming, and not scalable, requiring manual generation and often taking months or years to produce sufficient data for new geographical locations, limiting the ability to train and validate AI systems before deployment.
Innovation Solution
The use of procedural rendering techniques that leverage sensor data from real environments, supplemented with tertiary data, to generate accurate and scalable simulated worlds, reducing computational resources and time required for generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual generation techniques are used to create simulated environments, then the environments can be customized and controlled, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The patent uses procedural rendering to generate simulated environments by copying and adapting real-world sensor data and map information. Instead of manually creating each environment element, the system procedurally generates realistic scenes by synthesizing data from multiple sources, dramatically reducing generation time while maintaining accuracy
Solution Approach 2:
The system changes parameters of existing data structures and sensor inputs to generate varied simulated environments. By adjusting procedural parameters such as lighting conditions, object placements, and environmental features while maintaining the underlying data structure, the system可以快速生成多样化的训练场景
2Reliability
If manual generation techniques are used to create simulated environments, then quality control is possible, but the time required to produce sufficient data increases to months or years
Solution Approach 1:
The system performs preliminary processing of sensor data and map information in advance, organizing and structuring the data so that procedural rendering can quickly generate environments. By pre-processing and storing essential environmental data in structured formats, the system eliminates time-consuming manual preparation steps
Solution Approach 2:
The system copies and synthesizes environmental data from multiple real-world sources to create simulated environments that maintain quality characteristics. By procedurally generating environments based on validated real-world data rather than manual creation, the system ensures reliability while dramatically reducing production time from months to minutes
3Manufacturing precision
If manual generation techniques are used, then detailed control over environment features is achieved, but scalability is limited
Solution Approach 1:
The patent creates a universal procedural rendering system that can generate multiple types of simulated environments from a single data processing pipeline. The system handles diverse environment types (urban, rural, various weather conditions) using the same core technology, enabling scalable generation across different geographical locations and scenarios without requiring separate manual generation processes for each
Data Source
AI summary
Procedural generation of simulated environments is described herein. In an example, a computing device can access road network data associated with a real environment. Further, the computing device can generate, based on the sensor data, a road mesh associated with the real environment. The computing device can associate the road network data with the road mesh to generate a simulated environment and can procedurally render at least one object into the simulated environment based at least in part on at least one rule. The computing device can output the simulated environment for at least one of testing, validating, or training an algorithm used by an autonomous vehicle for at least one of navigating, planning, or decision making.