The Role of Vehicles in Digital Forensic Investigations: A Structured Synthesis of Digital Vehicle Forensic Characteristics
2026-06-29 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors explain that modern vehicles have lots of digital data spread across many places, making it hard to investigate accidents or crimes involving them. They define digital vehicle forensics (DVF) as collecting and analyzing this data carefully while following safety and privacy rules. They describe the main challenges, like multiple users and complex networked systems, and propose a way to prioritize which data to examine first. Their work provides a clear framework for investigators to understand and approach vehicle digital evidence without relying on specific tools or algorithms.
Digital Vehicle ForensicsCyber-Physical SystemsEvidence TriageData VolatilityForensic SoundnessNetworked SystemsPrivacy ConstraintsSafety ImplicationsData AcquisitionAdversarial Perspective
Authors
Kevin Mayer
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.