3D Virtual World Simulation With Realistic Sensor Noise Modeling
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Solution Overview
Problem
Current autonomous driving simulators rely on overly simplified vehicle models and lack realistic sensory data, which hinders the training and validation of autonomous driving systems, particularly in complex geographical areas with diverse traffic scenarios.
Innovation Solution
A system and method that create a simulated model of a geographical area using a semantic-data dataset, enhanced by noise patterns from various vehicle sensors, to generate realistic sensory data for training autonomous driving systems, employing neural networks like GANs for improved accuracy and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplified vehicle models are used in autonomous driving simulators, then device complexity is reduced, but measurement precision and realism of sensory data deteriorate
Solution Approach 1:
The patent creates virtual copies of real-world geographical areas, traffic scenarios, and sensor data by training neural networks on real sensor data and using these networks to generate realistic synthetic sensory data in simulated environments, thereby achieving high measurement precision without requiring complex physical models
Solution Approach 2:
The patent replaces traditional mechanical/physical simulation models with neural network-based systems that process and generate sensory data, substituting complex physical computations with learned patterns from real-world data, thus maintaining realism while simplifying the underlying system
2Measurement precision
If realistic sensory data with noise patterns is generated, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary training of neural networks on real sensor data before deployment, so that when the system runs simulations, the pre-trained networks can generate realistic sensory data with appropriate noise patterns without requiring complex real-time processing, thus reducing operational complexity
Solution Approach 2:
The patent introduces neural networks as intermediary components between the simulated environment and the sensory data generation process, where these networks act as mediators that translate simple environmental parameters into realistic sensor readings with appropriate noise characteristics
3Adaptability or versatility
If complex geographical areas with diverse traffic scenarios are simulated, then adaptability improves, but productivity decreases due to increased computation time
Solution Approach 1:
The patent uses neural networks to copy and replicate complex real-world traffic scenarios and geographical areas in virtual environments, allowing diverse and adaptable simulations to run efficiently by leveraging learned patterns rather than requiring exhaustive computation of all possible scenarios
4Measurement precision
If noise patterns from various sensors are applied, then measurement precision improves, but loss of information increases due to data processing complexity
Solution Approach 1:
The patent applies noise patterns by modifying parameters of the generated sensory data to match statistical characteristics of real sensor readings, thereby improving measurement precision through parameter adjustment rather than complex data processing that would cause information loss
Data Source
AI summary
A computer implemented method of creating data for a host vehicle simulation, comprising: in each of a plurality of iterations of a host vehicle simulation using at least one processor for: obtaining from an environment simulation engine a semantic-data dataset representing a plurality of scene objects in a geographical area, each one of the plurality of scene objects comprises at least object location coordinates and a plurality of values of semantically described parameters; creating a 3D visual realistic scene emulating the geographical area according to the dataset; applying at least one noise pattern associated with at least one sensor of a vehicle simulated by the host vehicle simulation engine on the virtual 3D visual realistic scene to create sensory ranging data simulation of the geographical area; converting the sensory ranging data simulation to an enhanced dataset emulating the geographical area, the enhanced dataset comprises a plurality of enhanced scene objects.