With the introduction of self-driving vehicles ensuring automotive safety and security stands as a critical challenge. This research thoroughly investigates the evolving threat landscape to identify new and emerging threats specific to autonomous vehicles (AVs), evaluate limitations and strengths the TARA methodology within the context of AV and identify effective mitigation strategies to safeguard AVs against cyber threats. Through systematic evaluation, the study identifies threats specific to AVs and proposes mitigations for threats in sensor signal interference and manipulation, physical environment manipulation and deception, and unauthorized access and control to make driving decisions. Threats from signal interference and manipulation can be reduced by implementing sensor fusion techniques, advanced cryptographic measures, redundancy and randomization. Physical manipulation and deception can be mitigated by implementing redundancy, physical and operational safeguards, and advanced cryptographic techniques. Unauthorized access and control in automated systems can be countered through advanced cryptographic measures, security protocols, and integrity strategies for software and firmware. The evaluation of the TARA methodology highlights its limitations in applying STRIDE threat modeling and feasibility ratings from experts, particularly when addressing non-traditional cybersecurity threats. These findings contribute to the development of a detailed ontology that categorizes assets, threats, and mitigation strategies specific to AVs, and the consideration of integrating “spoofing” from the STRIDE threat model with “environment” to comprehensively capture AV threats. This study thereby enhances the understanding and management of cybersecurity risks in the automotive industry.