  In this paper, we present a hierarchical framework for decision-making and
planning on highway driving tasks. We utilized intelligent driving models (IDM
and MOBIL) to generate long-term decisions based on the traffic situation
flowing around the ego. The decisions both maximize ego performance while
respecting other vehicles' objectives. Short-term trajectory optimization is
performed on the Frenet space to make the calculations invariant to the road's
three-dimensional curvatures. A novel obstacle avoidance approach is introduced
on the Frenet frame for the moving obstacles. The optimization explores the
driving corridors to generate spatiotemporal polynomial trajectories to
navigate through the traffic safely and obey the BP commands. The framework
also introduces a heuristic supervisor that identifies unexpected situations
and recalculates each module in case of a potential emergency. Experiments in
CARLA simulation have shown the potential and the scalability of the framework
in implementing various driving styles that match human behavior.
