  To improve safety and energy efficiency, autonomous vehicles are expected to
drive smoothly in most situations, while maintaining their velocity below a
predetermined speed limit. However, some scenarios such as low road adherence
or inadequate speed limit may require vehicles to automatically adapt their
velocity without external input, while nearing the limits of their dynamic
capacities. Many of the existing trajectory planning approaches are incapable
of making such adjustments, since they assume a feasible velocity reference is
given. Moreover, near-limits trajectory planning often implies high-complexity
dynamic vehicle models, making computations difficult. In this article, we use
a simple dynamic model derived from numerical simulations to design a
trajectory planner for high-speed driving of an autonomous vehicle based on
model predictive control. Unlike existing techniques, our formulation includes
the selection of a feasible velocity to track a predetermined path while
avoiding obstacles. Simulation results on a highly precise vehicle model show
that our approach can be used in real-time to provide feasible trajectories
that can be tracked using a simple control architecture. Moreover, the use of
our simplified model makes the planner more robust and yields better
trajectories compared to kinematic models commonly used in trajectory planning.
