Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving

The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becoming of utmost importance. Simulation suites allow the used of automated validation techniques in a wide variety of scenarios, and enable the development of closed-loop validation methods, such as machine learning and reinforcement learning approaches. However, simulation tools suffer from a standing flaw in that there is a noticeable gap between the simulation conditions and real-world scenarios. Although the use of simulators powers most of the research around autonomous driving, and is generally used within all domains it is divided into, there is an inherent source of error given the stochastic nature of activities performed in real world, which are unreplicable in computer environments. This paper proposes a new approach to assess the real-to-sim gap for path tracking systems. The aim is to narrow down the sources of error between simulation results and real-world conditions, and to evaluate the performance of the simulation suite in the design process by employing the information extracted from gap analysis, which adds a new dimension of development against other approaches for autonomous driving. A real-time model predictive controller (MPC) based on adaptive potential fields was developed and validated using the CARLA simulator. Both the path planning and vehicle control systems where tested in real traffic conditions. The error between the simulator and the real data acquisition was evaluated using the Pearson correlation coefficient (PCC) and the max normalized cross-correlation (MNCC). The controller was further evaluated on a process of sim-to-real transfer, and was finally tested both in simulation and real traffic conditions. A comparison was performed against an optimal-control ILQR-based model predictive controller was carried out to further showcase the validity of this approach.