Geometry-Evolving Barrier Functions for Safety Evaluation in Dynamic Environments

Abstract

Autonomous systems operating in dynamic environments often involve multiple interacting agents, giving rise to high-dimensional safety evaluation problems. Hamilton--Jacobi (HJ) reachability provides rigorous finite-horizon safety guarantees, but solving a global HJ partial differential equation (PDE) over the entire state space leads to prohibitive computational complexity. In this paper, we introduce Geometry-Evolving Barrier Functions (GEBFs), a novel representation for dynamic safety evaluation that propagates local safety geometry of the solution to a terminal-value HJ PDE. By evolving the value, gradient, and curvature information along a Hamiltonian-induced reference trajectory, GEBFs provide a compact representation of the local HJ safety landscape. Under suitable local regularity assumptions, we establish consistency between the GEBF and the value, gradient, and Hessian of the HJ solution along an HJ-optimal characteristic. Building on these theoretical results, we develop an iterative algorithm that couples local geometric propagation with trajectory refinement. We further establish a trajectory-wise one-sided relationship between the GEBF and HJ value function, together with a fixed-point consistency result for the iterative scheme. Experiments on widely adopted agent--obstacle collision-avoidance benchmarks, across low- and high-dimensional systems, demonstrate that GEBFs accurately capture the evolution of dynamic safety boundaries, while achieving substantially lower computational cost than conventional HJ reachability.