Kaufeld, Marc (Technical University of Munich), Piccinini, Mattia (Technical University of Munich), Betz, Johannes (Technical University of Munich)

MP-RBFN: Learning-Based Vehicle Motion Primitives Using Radial Basis Function Networks

Scheduled for presentation during the Regular Session "S26a-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (TH-LM-T26), Thursday, November 20, 2025, 10:50−11:10, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for autonomous driving. While traditional motion planning approaches based on optimization are highly accurate, they are often computationally prohibitive. In contrast, sampling-based methods demonstrate high performance but impose constraints on the geometric shape of trajectories. MP-RBFN combines the strengths of both by coupling the high-fidelity trajectory generation of sampling-based methods with an accurate description of vehicle dynamics. Empirical results show compelling performance compared to previous methods, achieving a precise description of motion primitives at low inference times. MP-RBFN yields a seven times higher accuracy in generating optimized motion primitives compared to existing semi-analytic approaches. We demonstrate the practical applicability of MP-RBFN for motion planning by integrating the method into a sampling-based trajectory planner.