The results of this sensitivity analysis test can be seen above. The number of samples was set to 200 arbitrarily. The sampling method selected for this experiment was the Latin hypercube. They indicate that collisions (shown with black stars in the figure) occur in situations of high leader deceleration and high vehicle speed.

Co-Simulation with CARLA: Merging Virtual and Real-World Testing

Once the human driver model has been calibrated and validated using the tools above, the effectiveness of ADS needs to be evaluated in an integrated simulation environment. In other words, we need to place an actual Autonomous Vehicle in an environment of human driven vehicles with the i4Driving model in Aimsun Next. In order to do this, we developed an all new interface between CARLA and Aimsun Next.  

CARLA is a high-fidelity open-source simulator for autonomous driving research. Our co-simulation platform allows for testing ADS in realistic, virtual environments that simulate various traffic, weather, and road conditions. In addition to that, the CARLA simulator allows all three of autonomous vehicle layers to be present in the co-simulation environment, allowing OEMs to effectively test their systems, while gaining the benefits of the sophisticated traffic environment of Aimsun Next. The combination of real-world data and simulation provides a comprehensive testing ground for autonomous systems.

CARLA’s advanced simulation capabilities enable us to include an ADS in the Aimsun Next environment, allowing ADS developers to test edge cases and complex situations in a controlled, safe environment. By leveraging CARLA, we can push the boundaries of ADS testing, offering a dynamic and scalable way to validate systems before they are deployed on public roads.

The image below shows the network developed for an initial test of the platform. This is one of the default maps included in CARLA, but also translated into Aimsun Next.