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In this paper, we present a hierarchical framework for decision-making andplanning on highway driving tasks. We utilized intelligent driving models (IDMand MOBIL) to generate long-term decisions based on the traffic situationflowing around the ego. The decisions both maximize ego performance whilerespecting other vehicles' objectives. Short-term trajectory optimization isperformed on the Frenet space to make the calculations invariant to the road'sthree-dimensional curvatures. A novel obstacle avoidance approach is introducedon the Frenet frame for the moving obstacles. The optimization explores thedriving corridors to generate spatiotemporal polynomial trajectories tonavigate through the traffic safely and obey the BP commands. The frameworkalso introduces a heuristic supervisor that identifies unexpected situationsand recalculates each module in case of a potential emergency. Experiments inCARLA simulation have shown the potential and the scalability of the frameworkin implementing various driving styles that match human behavior.

An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space