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EthicaDrive
AI-driven autonomous vehicle simulator for accelerated development, optimized learning, and enhanced performance.
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Created on 31st October 2024
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EthicaDrive
AI-driven autonomous vehicle simulator for accelerated development, optimized learning, and enhanced performance.
The problem EthicaDrive solves
EthicaDrive effectively addresses the following challenges:
- Simulation Complexity: Provides a simplified 2D simulation environment for self-driving car testing, reducing computational requirements.
- Map Customization: Utilizes OpenStreetMap API for customizable map scenarios, enhancing testing flexibility.
- Autonomous Control: Employs feed-forward neural networks for efficient vehicle control, demonstrating potential for real-world applications.
- Visualization: Offers MiniMap and bird's eye view for improved situational awareness and user experience.
Challenges I ran into
During the development of EthicaDrive, the following challenges were encountered:
- Integration Complexity: Combining TensorFlow, PyTorch, and OpenStreetMap API presented technical integration hurdles.
- Neural Network Optimization: Training and optimizing feed-forward neural networks for vehicle control required significant computational resources and tuning efforts.
- Simulation Realism: Balancing simulation simplicity with realism to accurately model real-world driving scenarios.
- Scalability: Managing the trade-off between simulation complexity and performance to ensure smooth operation.
Technologies used
Discussion
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