This project is a simulation of an autonomous vehicle using the CARLA simulator. The vehicle is equipped with various sensors to perceive its environment and make driving decisions based on sensor data.
- Python 3.7 or higher
- CARLA Simulator (version 0.9.12 or higher)
- Required Python packages:
- carla
- numpy
- opencv-python (cv2)
- open3d
- matplotlib
- Install CARLA Simulator by following the instructions on the official website.
- Clone this repository to your local machine.
- Install the required Python packages:
pip install -r requirements.txt
Running the Simulation Start the CARLA server by navigating to the CARLA directory and running: bash Copy code ./CarlaUE4.sh In a separate terminal, navigate to the directory containing this project and run: bash Copy code python Carla.py Sensors The autonomous vehicle is equipped with the following sensors:
RGB Camera Depth Camera GNSS (GPS) IMU (Inertial Measurement Unit) Semantic Lidar Traffic Light Detector Speed Limit Detector Radar Sensor Lane Invasion Detector Vehicle Control The vehicle's behavior is controlled based on the data received from the sensors. It follows traffic rules, avoids collisions, and stays within lane boundaries.
Rules and Reinforcement Learning The vehicle's driving behavior is evaluated based on a set of predefined rules. Points are awarded or deducted based on the vehicle's adherence to these rules. This forms the basis for a reinforcement learning model where the vehicle learns to drive autonomously by maximizing its reward.