  In this work, we address the motion planning problem for autonomous vehicles
through a new lattice planning approach, called Feedback Enhanced Lattice
Planner (FELP). Existing lattice planners have two major limitations, namely
the high dimensionality of the lattice and the lack of modeling of agent
vehicle behaviors. We propose to apply the Intelligent Driver Model (IDM) as a
speed feedback policy to address both of these limitations. IDM both enables
the responsive behavior of the agents, and uniquely determines the acceleration
and speed profile of the ego vehicle on a given path. Therefore, only a spatial
lattice is needed, while discretization of higher order dimensions is no longer
required. Additionally, we propose a directed-graph map representation to
support the implementation and execution of lattice planners. The map can
reflect local geometric structure, embed the traffic rules adhering to the
road, and is efficient to construct and update. We show that FELP is more
efficient compared to other existing lattice planners through runtime
complexity analysis, and we propose two variants of FELP to further reduce the
complexity to polynomial time. We demonstrate the improvement by comparing FELP
with an existing spatiotemporal lattice planner using simulations of a merging
scenario and continuous highway traffic. We also study the performance of FELP
under different traffic densities.
