Algorithm Development for Avoiding Both Moving and Stationary Obstacles in an Unstructured High-Speed Autonomous Vehicular Application Using a Nonlinear Model Predictive Controller

The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model. Further, a new algorithm is developed based on the box slope and box detection methods to process the stationary and moving obstacles. These methods use the vehicle’s current heading and generate a light detection and ranging (LIDAR) view through rectangular box regions. These box regions mimic the actual vision sensor regions, and logic can easily be applied to real vehicle conditions. From the results, it is observed that the vehicle avoids both moving and stationary lengthy obstacles and can safely navigate through a pool of obstacles that can mimic real-world off-road scenarios. Further, the simulations with Gaussian noise in vehicle state estimations and obstacle states confirmed that the developed algorithm can reach the target without collision by meeting all the vehicle safety constraints for the considered uncertainty limits.