Research & Simulation of Cooperative Autonomous Vehicle Navigation
Note: The image is for demonstration purposes only. It does not represent the actual software interface, and the software name is not accurate.
Project Overview
A comprehensive research and simulation initiative focused on intelligent decision-making and cooperative navigation strategies for autonomous vehicles. Using a high-fidelity simulation environment, the project models dynamic traffic scenarios to analyze vehicle behavior, route optimization, and inter-vehicle communication under realistic urban conditions. While the core innovation remains confidential per client agreement, the study successfully demonstrated enhanced navigation reliability and efficiency through adaptive vehicle-to-vehicle (V2V) coordination.
Key Features
- Real-time traffic scenario generation and dynamic route planning from origin to destination.
- Behavior modeling of autonomous agents under varying environmental and traffic conditions.
- Evaluation of cooperative driving strategies to improve safety and throughput.
- Scenario-based performance benchmarking and iterative refinement of decision logic.
Technology Stack
- Simulation Frameworks: SUMO (traffic modeling), OMNeT++ (discrete-event network simulation)
- Middleware & APIs: TraCI (real-time traffic control interface), Veins (V2X simulation framework)
- Networking Layer: INET Framework (for realistic wireless communication modeling)
- Scripting & Analysis: Python (scenario automation, data extraction, and post-simulation analytics)