Physics joins AI: A real-time hybrid misbehavior detection framework for vehicular networks

Lorenzo Ghiro; Cristina Pezzoni; Marco Franceschini; Renato Lo Cigno

doi:10.1016/j.comcom.2026.108519

April 1, 2026Computer CommunicationsOpen Access

Physics joins AI: A real-time hybrid misbehavior detection framework for vehicular networks

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Key Points

The aim is to develop a trustworthy detection framework for communication messages in vehicular networks that combines AI and physical coherence.
Proposed a hybrid framework integrating AI analysis and physical coherence assessments.
Utilized the VeReMi dataset for performance evaluation.
Simulated highway scenarios using Plexe for real-time execution testing.
Developed a protocol for safely dismantling vehicle platoons when misbehaving messages are identified.
Achieved high performance in message evaluation on the VeReMi dataset.
Demonstrated effective real-time execution in highway simulations.
Showed reliability in assessing message validity from both trusted and untrusted sources.

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Implication

This work demonstrates a novel framework for real-time message validity assessment in vehicular networks, indicating improved trust in autonomous driving systems.

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Ghiro et al. (2026) studied this question.

synapsesocial.com/papers/69cf5f505a333a821460e688 https://doi.org/10.1016/j.comcom.2026.108519

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Physics joins AI: A real-time hybrid misbehavior detection framework for vehicular networks

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Abstract

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Physics joins AI: A real-time hybrid misbehavior detection framework for vehicular networks

Key Points

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