Off Road Autonomous Vehicle Modeling and Repeatability Using Real World Telemetry via Simulation

One approach to autonomous control of high mobility ground vehicle platforms operating on challenging terrain is with the use of predictive simulation. Using a simulated or virtual world, an autonomous system can optimize use of its control systems by predicting interaction between the vehicle and ground as well as the vehicle actuator state. Such a simulation allows the platform to assess multiple possible scenarios before attempting to execute a path. Physically realistic simulations covering all of these domains are currently computationally expensive, and are unable to provide fast execution times when assessing each individual scenario due to the use of high simulation frequencies (> 1000Hz). This work evaluates using an Unreal Engine 4 vehicle model and virtual environment, leveraging its underlying PhysX library to build a simple unmanned vehicle platform. The simulation is demonstrated to successfully run at low simulation frequencies down to a lower threshold of 190Hz with minimal average cross-track-error and heading angle deviation when performing multiple real off road driving maneuvers. Real vehicle telemetry was used as input to drive the unmanned vehicle's integrated Pure Pursuit and PID autonomous driving control algorithms within the simulation and used as ground truth for comparison.