Safe and Resilient Multi-vehicle Trajectory Planning Under Adversarial Intruder

TL;DR

This paper enhances multi-vehicle trajectory planning by developing a scalable, safe, and resilient algorithm that limits replanning to a fixed subset of vehicles, effectively handling adversarial intruders in complex environments.

Contribution

The paper introduces a novel algorithm that reduces replanning complexity in multi-vehicle systems, making trajectory planning more practical and resilient against adversarial intruders.

Findings

Replanning is limited to a fixed number of vehicles regardless of total system size.

The algorithm maintains safety and scalability in urban airspace simulations.

Effective handling of adversarial intruders in multi-vehicle trajectory planning.

Abstract

Provably safe and scalable multi-vehicle trajectory planning is an important and urgent problem. Hamilton-Jacobi (HJ) reachability is an ideal tool for analyzing such safety-critical systems and has been successfully applied to several small-scale problems. However, a direct application of HJ reachability to multi-vehicle trajectory planning is often intractable due to the "curse of dimensionality." To overcome this problem, the sequential trajectory planning (STP) method, which assigns strict priorities to vehicles, was proposed, STP allows multi-vehicle trajectory planning to be done with a linearly-scaling computation complexity. However, if a vehicle not in the set of STP vehicles enters the system, or even worse, if this vehicle is an adversarial intruder, the previous formulation requires the entire system to perform replanning, an intractable task for large-scale systems. In this paper, we make STP more practical by providing a new algorithm where replanning is only needed only for a fixed number of vehicles, irrespective of the total number of STP vehicles. Moreover, this number is a design parameter, which can be chosen based on the computational resources available during run time. We demonstrate this algorithm in a representative simulation of an urban airspace environment.