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

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