Abstract
In order to safely navigate highly dynamic scenarios, automated vehicles must be able to react quickly to changes in the environment and be able to understand trade-offs between lateral and longitudinal forces when limited by tire-road friction. We present a design and experimental validation of a nonlinear model predictive controller that is capable of handling these complex situations. By carefully selecting the vehicle model and mathematical encodings of the vehicle and obstacles, we enable the controller to quickly compute inputs while maintaining an accurate model of the vehicle's motion and its proximity to obstacles. Experimental results of a test vehicle performing an emergency double lane change to avoid two “pop-up” obstacles demonstrate the ability of the controller to coordinate lateral and longitudinal tire forces even in emergency situations when the tires are at their friction limits.
Highlights
A S AUTOMATED vehicles become more developed, they will need to handle a wide range of real-world situations
Model predictive control (MPC) has become a popular technique that combines the predictive power of motion planning with the speed and robustness of real-time control
Falcone et al [1] used nonlinear MPC (NMPC) to compute steer angles to track a path during a double lane change and analyzed how incorporating braking forces affected the problem complexity [6]
Summary
A S AUTOMATED vehicles become more developed, they will need to handle a wide range of real-world situations. Falcone et al [1] used nonlinear MPC (NMPC) to compute steer angles to track a path during a double lane change and analyzed how incorporating braking forces affected the problem complexity [6] They presented a controller that modeled the total forces on each tire but noted that the model complexity limited real-time implementations. Liu et al [7] and Febbo et al [8] investigated obstacle avoidance tasks, planning steering angle and a reference speed profile every 0.5 s While these algorithms were able to maneuver through complex environments using nonlinear models, the long solve times limit fast reactions to changes in. Video of this experiment is available at https://purl.stanford.edu/kw432sz0082
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