Spatio-Temporal Lattice Planning Using Optimal Motion Primitives
This work addresses motion planning for autonomous vehicles, presenting an incremental improvement in lattice-based techniques.
The paper tackles the motion planning problem for autonomous vehicles by formulating a mixed integer linear program to compute a minimal t-spanning set of motion primitives and proposing an A*-based algorithm with oscillation removal, validated in parking lot and highway scenarios.
Lattice-based planning techniques simplify the motion planning problem for autonomous vehicles by limiting available motions to a pre-computed set of primitives. These primitives are then combined online to generate more complex maneuvers. A set of motion primitives t-span a lattice if, given a real number t at least 1, any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than a factor of t from optimal. Computing a minimal t-spanning set balances a trade-off between computed motion quality and motion planning performance. In this work, we formulate this problem for an arbitrary lattice as a mixed integer linear program. We also propose an A*-based algorithm to solve the motion planning problem using these primitives. Finally, we present an algorithm that removes the excessive oscillations from planned motions -- a common problem in lattice-based planning. Our method is validated for autonomous driving in both parking lot and highway scenarios.