## 7 Conclusions and Future Work  
This study introduces a novel hybrid model that integrates expert-driven analysis with generative AI capabilities to enhance vulnerability scoring for sensor systems in autonomous vehicles. Our literature review and expert validation identified 15 key attack types, with the LiDAR sensor being the most targeted (9 attacks). CVSS v4.0 scores from 10 experts revealed that the most severe threats--eavesdropping (7.19), Sybil (6.76), and replay (6.35)--are predominantly network based, low complexity (92.3%), and require no privileges (51.3%) or user interaction (82.9%). AI models, when applied to a 117-entry vulnerability dataset, performed well on common classes: DeepSeek achieved 99.07% F1 for network vectors, ChatGPT led in low-complexity extraction, and Gemini applied a worst-case impact principle that favored cautious scoring in ambiguous cases. However, all models failed to accurately capture rare high-impact classes, with F1 scores dropping to 0%-15% for attributes like High Complexity and Changed Scope. These results underscore the effectiveness of our hybrid approach in achieving better contextualized and semi-automated scoring, while also highlighting critical gaps in current AI model capabilities when applied to specialized domains like automotive cybersecurity. Future research will focus on three primary directions to enhance the proposed model:  
1. Domain-Specific Fine-Tuning of AI Models: Current generative AI models lack training on automotive cybersecurity datasets. Future work should involve fine-tuning LLMs using domain-specific corpora, including CVEs, threat intelligence feeds, and technical whitepapers related to vehicular sensor systems.
2. Integration of Real-Time Threat Intelligence: Enhancing the model with dynamic threat feeds and anomaly detection tools could improve responsiveness to emerging attack vectors and zero-day vulnerabilities.
3. Multi-Modal Vulnerability Analysis: Future iterations should consider incorporating additional data modalities (e.g., sensor logs and CAN bus traffic) for deeper behavioral analysis and richer context in vulnerability assessment.
4. Improved Scoring Mechanisms for Physical Attacks: Since physical-layer threats are currently underweighted by CVSS and misclassified by AI models, research should aim to augment or revise scoring criteria to better capture their practical implications in vehicular environments.  
By pursuing these avenues, the proposed model can evolve into a robust, scalable, and adaptive solution for next-generation automotive cybersecurity risk assessment.  
Conceptualization, H.K.A. and I.T.A.H.; methodology, H.K.A. and I.T.A.H.; software, M.S.F.; validation, M.S.F., H.K.A. and I.T.A.H.; formal analysis, I.T.A.H. and M.S.F.; investigation, H.K.A. and M.S.F.; resources,M.S.F., H.K.A. and I.T.A.H.; data curation, M.S.F., H.K.A. and I.T.A.H.; writing--original draft preparation, M.S.F.; writing--M.S.F., H.K.A. and I.T.A.H.; visualization, M.S.F.; supervision, H.K.A. and I.T.A.H.; project administration, H.K.A. and I.T.A.H.; funding acquisition, M.S.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest