The development of autonomous electric vehicles (AEVs) represents the convergence of two simultaneous automotive revolutions: electric vehicles (EVs) and autonomous vehicles (AVs). AVs require sensors

decision-making systems and actuation systems to achieve autonomous driving

whereas EVs require intelligent management and real-time communication with the electricity grid and charging facilities. AEVs integrate these domains into a connected

intelligent and energy-conscious cyber-physical system. For example

advanced driver assistance systems (ADAS)

installed in AEVs

allow the vehicle to operate without human interaction. However

this interconnectedness of AEVs through ADAS and other highly connected systems significantly increases the attack surface. Threats to AEVs include artificial intelligence manipulation and sensor impersonation

whereas vulnerabilities in network communications

charging protocols and remote access can further expand their threat surface. Complex cyber-physical threat vectors arise from the interaction of the AEV components across external (5G

Wi-Fi) and internal (Control Area Network [CAN]

Ethernet) communication networks. Additional entry points arise through the exchange of data and power between charging stations and the network infrastructure. These threats can impact vehicle charging

energy consumption

damage vehicle engines and can even adversely affect the entire grid system. However

current research is often siloed either focussing on charging systems or on vehicles independently lacking unified models that capture common threat paths

attack surfaces and security interconnections particularly for advanced driver assistance systems. AEVs communicate to other vehicles

infrastructure

grid

network through Vehicle-to-Vehicle (V2V)

Vehicle-to-Infrastructure (V2I)

Vehicle-to-Grid (V2G)

Vehicle-to-Network (V2N) channels of communication

all of which are grouped into the larger Vehicle-to-Everything (V2X) communication. Every connection is thus a potential entry point for cyber attacks. In this paper

we present an early threat landscape model represented as a formal ontology of AEV components

threats

vulnerabilities and mitigations

which are mapped to threat frameworks to provide a detailed view. The semantic representation of the AEV threat landscape provides a machine-interpretable knowledge model that enables automated reasoning and rich querying to identify hidden risks and trace attack propagation paths unlike static models or flat frameworks. The modular and extensible nature of the ontology is most suited to the ever-evolving and rapidly changing nature of AEVs and their threats.