Work

Physics Joins AI: A Real-Time Hybrid Misbehavior Detection Framework for Vehicular Networks

Year: 2026

Type: article

Abstract: Trusting autonomous, connected vehicles is necessary to build Cooperative Driving applications and enhance Smart Mobility. A necessary step is full confidence in communications beyond identification, pseudonyms and certificates: Message validity must be unquestionable to enable cooperation, even when messages originate from trusted entities affected by faults or malfunctions. Extreme reliability requires multiple evaluation tools, as independent as they can be, to fuse estimation into a dependable decision. This work proposes to combine two methods of evaluating messages, one based on Artificial Intelligence (AI) analysis and one on physical coherence of message content, achieving extremely good performance both on the VeReMi dataset, and in run-time execution in a highway scenario simulated with Plexe . Moreover, the paper proposes a simple protocol demonstrating how to safely dismantle a platoon of Cooperative Driving vehicles returning to autonomous (or human) driving when misbehaving messages are received.

Source: Computer Communications

DOI: 10.1016/j.comcom.2026.108519

Authors: Lorenzo Ghiro, Cristina Pezzoni, Marco Franceschini, Renato Lo Cigno

Institutions: Consorzio Nazionale Interuniversitario per le Telecomunicazioni, University of Brescia

Language: English

Topic: Vehicular Ad Hoc Networks (VANETs)

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,620