Developing a self-driving car simulation can be a complex task and typically involves the following steps:

Define the virtual environment: This includes creating a realistic 3D model of the roads, buildings, and other objects that the car will navigate through. The environment should also include simulated sensor data, such as lidar and camera feeds, that the car can use to perceive its surroundings.

Develop a control system: This includes creating a set of algorithms that can take in sensor data and output control commands for the car, such as steering and acceleration. The control system should also include logic for decision-making, such as how to navigate through traffic or avoid obstacles.

Train and test the system: Use a dataset of sensor data and corresponding control commands to train the control system using machine learning techniques. Once the system is trained, test it in the virtual environment to evaluate its performance and make any necessary adjustments.

Integrate the system into the simulation: Once the control system is working as desired, integrate it into the virtual environment so that the car can navigate through the simulation.

Repeat testing with different scenarios: Once the car is integrated, test it on different scenarios to ensure the vehicle can handle different situations.

It is important to note that this is a high-level overview and each step may require significant effort and expertise.