The Role of Vehicles in Digital Forensic Investigations: A Structured Synthesis of Digital Vehicle Forensic Characteristics

• URL: http://arxiv.org/abs/2606.30564v1
• Date: Mon, 29 Jun 2026 17:06:53 GMT
• Title: The Role of Vehicles in Digital Forensic Investigations: A Structured Synthesis of Digital Vehicle Forensic Characteristics
• Abstract summary: Digital vehicle forensics (DVF) remains less mature than computer, mobile, and cloud forensics.<n>This article addresses this gap through a structured synthesis of academic literature, standards, and practitioner-oriented sources.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern vehicles are cyber-physical, networked systems that may contain valuable digital traces for accident reconstruction, crime investigation, warranty analysis, and cybersecurity incident response. However, digital vehicle forensics (DVF) remains less mature than computer, mobile, and cloud forensics because relevant data is distributed across in-vehicle components, mobile devices, manufacturer back ends, third-party services, and physical evidence. This article addresses this gap through a structured synthesis of academic literature, standards, and practitioner-oriented sources. First, we define DVF as the identification, preservation, acquisition, verification, interpretation, and reporting of vehicle-related digital evidence under safety, legal, privacy, and forensic-soundness constraints. Second, we formalize the DVF triage problem as the selection and correlation of evidence sources subject to volatility, accessibility, safety, integrity, and authorization constraints. Third, we explain how eight characteristics were derived from the literature and case material: multiple users, massively networked, cyber-physical system, dependencies between components, functional data, safety implications, accessibility, and limited abstraction. Finally, we add an adversarial perspective and a characteristic-driven triage procedure that helps investigators prioritize evidence sources while documenting assumptions, limitations, and failure cases. The resulting contribution is not an algorithmic performance claim; it is a reproducible conceptual framework for understanding, planning, and communicating DVF investigations.

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