Online Autonomous Driving Simulation Platform
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Solution Overview
Problem
Existing autonomous driving simulation systems lack accessibility and fidelity, requiring users to build simulation environments and using standard traffic models that fail to accurately replicate real human driver behavior.
Innovation Solution
An online central simulation environment where multiple vehicle models with different algorithms and sensors can interact, with a system comprising a communication server, environment server, experiment server, and database to render 3D environments and manage simulation sessions, allowing for continuous generation of sensor data and integration of new vehicle models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users build the simulation environment themselves using related art solutions, then the system can be customized, but the accessibility and ease of operation deteriorate
Solution Approach 1:
The system provides pre-built simulation environments and vehicle models that users can directly utilize without needing to construct them themselves. The platform automatically manages the complexity of environment creation while allowing users to focus on their specific testing needs through configurable parameters and modular components.
Solution Approach 2:
The platform creates a universal simulation environment that can accommodate multiple vehicle models, sensor types, and test scenarios through a single unified system. This eliminates the need for users to build separate environments for different testing requirements, improving accessibility while maintaining versatility through standardized interfaces and configurable parameters.
2Ease of manufacture
If standard traffic models like cellular automata and car-following models are used, then the implementation is simple, but the fidelity and measurement precision deteriorate
Solution Approach 1:
The system transitions from fixed-parameter standard traffic models to dynamic parameter models that adapt based on real-world driving data. By incorporating machine learning algorithms and adjusting model parameters based on actual human driver behavior patterns, the system maintains implementation feasibility while significantly improving forecast accuracy and behavioral fidelity.
Solution Approach 2:
The patent replaces traditional mechanical traffic models with data-driven machine learning models that learn from real driving data. This substitution allows the system to capture complex human driver behaviors that cannot be represented by simple mathematical relationships, thereby improving measurement precision while maintaining computational efficiency through optimized algorithms.
3Reliability
If physical road tests are conducted to train deep learning neural networks, then the data is realistic, but the time consumption and productivity deteriorate
Solution Approach 1:
The system creates virtual copies of real-world driving scenarios through high-fidelity simulation environments. By replicating road conditions, weather patterns, traffic situations, and sensor responses in the virtual environment, the system generates realistic training data without the time and resource constraints of physical road tests, thereby improving productivity while maintaining data realism.
Solution Approach 2:
The platform performs preliminary simulation and data generation before actual road tests are needed. By pre-generating diverse training scenarios, edge cases, and labeled datasets in the virtual environment, the system reduces the time required for physical testing and accelerates the overall development cycle while maintaining data quality through realistic simulation physics and sensor models.
4Quantity of substance
If a fleet of 20 test vehicles is deployed for road testing, then comprehensive data can be collected, but the resource consumption and cost increase
Solution Approach 1:
The system replaces physical test vehicles with virtual vehicle models in simulation environments. By creating and testing multiple virtual vehicle instances simultaneously, the system can generate comparable or superior data volumes without the resource consumption, maintenance costs, and logistical overhead of deploying a large fleet of physical vehicles, thereby reducing energy loss while maintaining data quantity.
Data Source
AI summary
Example implementations described herein facilitate an interactive environment for companies and personals to validate and develop autonomous driving systems. Such implementations apply to, but are not limited to, applications such as sensor data collection for deep learning model training; validation and development of various detection algorithms; sensor fusion (e.g., radar, lidar, camera) algorithm development and validation, trajectory/motion planning algorithm validation; and control algorithm validation.