This project focuses on integrating multiple radar and lidar sensors mounted on vehicles to create a dynamic simulation of a road environment. By utilizing MATLAB's Automated Driving Toolbox, we capture real-time data, providing a comprehensive bird’s-eye view of the surrounding traffic and enhancing situational awareness for autonomous driving applications.
The idea for this project emerged from the growing need for advanced driver assistance systems (ADAS) that require accurate and reliable perception of the environment. By simulating various scenarios with radar and lidar sensors, we aim to refine the algorithms used for object detection and tracking in autonomous vehicles.
- Dynamic Environment Simulation: Create a realistic representation of traffic scenarios using MATLAB, with multiple vehicles equipped with radar and lidar sensors.
- Advanced Sensing Capabilities: Leverage the unique capabilities of radar and lidar to detect and classify surrounding objects accurately.
- Bird’s-Eye View Visualization: Generate a comprehensive overview of the traffic situation, aiding in decision-making for autonomous driving systems.
- Radar Sensors: Utilize multiple radar units to gather distance and speed data from surrounding vehicles.
- Lidar Sensors: Implement lidar systems to create detailed 3D maps of the environment.
- MATLAB: Ensure you have the latest version of MATLAB installed, along with the Automated Driving Toolbox.
- Simulink: Use Simulink for simulating the vehicle dynamics and sensor fusion algorithms.
- Set Up MATLAB: Install the necessary toolboxes including the Automated Driving Toolbox.
- Simulate Traffic Scenarios: Create multiple traffic scenarios within the MATLAB environment to test the integration of radar and lidar sensors.
- Analyze Data: Utilize MATLAB's visualization tools to interpret and analyze the data collected during simulations.
Contributions to the Multiple Sensor Integration project are welcome! Whether you're interested in improving the simulation, enhancing sensor models, or exploring new algorithms for data analysis, feel free to share your insights and suggestions. Together, we can push the boundaries of autonomous driving technology!
