Spatio-Temporal Lattice Planning Using Optimal Motion Primitives

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 11; pp. 1 - 13
• Main Authors: Botros, Alexander, Smith, Stephen L.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; autonomous driving; Autonomous vehicles; Computation; Integer programming; Interpolation; Lattices; Mixed integer; Motion planning; Oscillators; path planning; Planning; Real numbers; Road transportation; Search problems; trajectory optimization; Vector spaces
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3297068

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 combined online to generate complex maneuvers. A set of motion primitives <inline-formula> <tex-math notation="LaTeX">t</tex-math> </inline-formula>-span a lattice if, given a real number <inline-formula> <tex-math notation="LaTeX">t\geq 1</tex-math> </inline-formula>, any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than a factor of <inline-formula> <tex-math notation="LaTeX">t</tex-math> </inline-formula> from optimal. Computing a minimal <inline-formula> <tex-math notation="LaTeX">t</tex-math> </inline-formula>-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 and 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.