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

2026-06-29

AI Summary

This study addresses the absence of a systematic framework in digital vehicle forensics (DVF), where evidence is fragmented across in-vehicle systems, mobile devices, manufacturer backends, and third-party services. Through a structured review of academic literature, standards, and real-world cases, this work identifies eight core characteristics of DVF for the first time, incorporates an adversarial perspective, formalizes the initial forensic triage problem, and proposes a feature-driven prioritization workflow. The resulting reproducible conceptual framework clarifies strategies for selecting and correlating evidence sources, significantly enhancing the efficiency and rigor of forensic investigations in accident reconstruction, criminal inquiries, and cybersecurity incident response—while explicitly accounting for safety, legal, and privacy constraints.

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.

Problem

• digital vehicle forensics
• evidence triage
• cyber-physical systems
• distributed digital evidence
• forensic investigation

Innovation

• Digital Vehicle Forensics
• Cyber-Physical Systems
• Evidence Triage
• Forensic Framework
• Connected Vehicles