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AI-Powered Real-Time Collision Avoidance in Autonomous Vehicles Using Random GAN-X
Authors
1 Department of Computer Science and Engineering, GIET University, Gunupur, Odisha, India. 2 Department of Computer Science Engineering, Garden City University, Bengaluru, Karnataka, India. 3 Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India. 4 Department of Engineering, Garden City University, Bengaluru, Karnataka, India. 5 Department of Computer Science Engineering, GIET University Gunupur, Odisha, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 186-192
- Article Type
- Research Article
- Check for updates
-
References
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9. Vargas, J., Alsweiss, S., Toker, O., Razdan, R., & Santos, J. (2021). An overview of autonomous vehicles sensors and their vulnerability to weather conditions. Sensors, 21(16), 5397.
10. Hussain, M. I., Azam, S., Rafique, M. A., Sheri, A. M., & Jeon, M. (2022). Drivable region estimation for self-driving vehicles using radar. IEEE Transactions on Vehicular Technology, 71(6), 5971-5982.
11. Xia, L., Sun, Y., Swash, R., Mohjazi, L., Zhang, L., & Imran, M. A. (2022). Smart and secure CAV networks empowered by AI-enabled blockchain: The next frontier for intelligent safe driving assessment. IEEE Network, 36(1), 197-204.
12. Alsulami, A. A., Al-Haija, Q. A., Alturki, B., Alqahtani, A., & Alsini, R. (2023). Security strategy for autonomous vehicle cyber-physical systems using transfer learning. Journal of Cloud Computing, 12(1), 181.
13. Kim, T. H., Ojo, S., Krichen, M., & Alamro, M. A. (2024). Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks. Electronics, 13(10), 1885.
14. Osman, R. A. (2023). Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach. Electronics, 12(19), 4023.
15. Cui, J., Chen, Y., Zhong, H., He, D., Wei, L., Bolodurina, I., & Liu, L. (2023). Lightweight encryption and authentication for controller area network of autonomous vehicles. IEEE Transactions on Vehicular Technology, 72(11), 14756-14770.
16. Khan, M. Z., Sarkar, A., Ghandorh, H., Driss, M., & Boulila, W. (2022). Information fusion in autonomous vehicle using artificial neural group key synchronization. Sensors, 22(4), 1652.
17. Raptis, G. E., Katsini, C., Alexakos, C., Kalogeras, A., & Serpanos, D. (2022). CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning. Applied Sciences, 12(22), 11631.
18. Vitale, C., Piperigkos, N., Laoudias, C., Ellinas, G., Casademont, J., Escrig, J., ... & Khodashenas, P. S. (2021). CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks. EURASIP Journal on Wireless Communications and Networking, 2021, 1-28.
19. Kumar, P., Kumar, R., Jolfaei, A., & Mohammad, N. (2024). An Automated Threat Intelligence Framework for Vehicle Road Cooperation Systems. IEEE Internet of Things Journal.
20. Rathore, M. S., Poongodi, M., Saurabh, P., Lilhore, U. K., Bourouis, S., Alhakami, W., ... & Hamdi, M. (2022). A novel trust-based security and privacy model for internet of vehicles using encryption and steganography. Computers and Electrical Engineering, 102, 108205.
21. Chen, D., & Lv, Z. (2022). Artificial intelligence enabled Digital Twins for training autonomous cars. Internet of Things and Cyber-Physical Systems, 2, 31-41.
22. Girdhar, M., You, Y., Song, T. J., Ghosh, S., & Hong, J. (2022). Post-accident cyberattack event analysis for connected and automated vehicles. IEEE Access, 10, 83176-83194.
23. Wang, L., & Zhang, X. (2023). Anomaly detection for automated vehicles integrating continuous wavelet transform and convolutional neural network. Applied Sciences, 13(9), 5525.
24. Sangapu, S. C., Prasad, K. N., Kannan, R. J., Chen, T. M., & Sathiyanarayanan, M. (2023). Impact of class imbalance in VeReMi dataset for misbehavior detection in autonomous vehicles. Soft Computing, 1-11.
25. Kakkar, R., Gupta, R., Agrawal, S., Tanwar, S., & Sharma, R. (2022). Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G. Journal of Information Security and Applications, 67, 103179.
26. Cao, M., Wang, R., Chen, N., & Wang, J. (2021). A learning-based vehicle trajectory-tracking approach for autonomous vehicles with lidar failure under various lighting conditions. IEEE/ASME transactions on mechatronics, 27(2), 1011-1022.
27. Khatab, E., Onsy, A., & Abouelfarag, A. (2022). Evaluation of 3D vulnerable objects’ detection using a multi-sensors system for autonomous vehicles. Sensors, 22(4), 1663.
28. Muzahid, A. J. M., Kamarulzaman, S. F., Rahman, M. A., & Alenezi, A. H. (2022). Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles. IEEE Access, 10, 43303-43319.
29. Chang, C. C., Ooi, Y. M., & Sieh, B. H. (2021). IoV-based collision avoidance architecture using machine learning prediction. IEEE Access, 9, 115497-115505.
30. Phung, K. H., Tran, H., Nguyen, T., Dao, H. V., Tran-Quang, V., Truong, T. H., ... & Steenhaut, K. (2021). OneVFC—A vehicular fog computation platform for artificial intelligence in Internet of Vehicles. IEEE Access, 9, 117456-117470
31. Driss, M., Almomani, I., e Huma, Z., & Ahmad, J. (2022). A federated learning framework for cyberattack detection in vehicular sensor networks. Complex & Intelligent Systems, 8(5), 4221-4235
32. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
33. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
34. Li, B.; Chen, H.; Lin, X.; Duan, H. Multimodal Learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: A multicenter diagnostic study. Int. J. Surg. 2024, 110, 3237–3248.
35. Zhu, L.; Lai, Y.; Ta, N.; Cheng, L.; Chen, R. Multimodal approach in the diagnosis of urologic malignancies: critical assessment of ChatGPT-4V’s image-reading capabilities. JCO Clin. Cancer Inform. 2024, 8, e2300275.
36. Lin, A.C.; Liu, Z.; Lee, J.; Ranvier, G.F.; Taye, A.; Owen, R.; Matteson, D.S.; Lee, D. Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study. Surgery 2024, 175, 121–127.
37. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
38. Wang, Y.; Chen, W.; Han, X.; Lin, X.; Zhao, H.; Liu, Y.; Zhai, B.; Yuan, J.; You, Q.; Yang, H. Exploring the reasoning abilities of multimodal large language models (mllms): A comprehensive survey on emerging trends in multimodal reasoning. arXiv 2024, arXiv:2401.06805.
39. Panda, R., Dash, S., Padhy, S., & Khandakhani, S. W. (2024). Visualizing and Understanding the Customized Convolutional Neural Networks to Identify Hand Written Odia Characters and its Pattern Using Generative AI.
40. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
2. Hassan, Z., Mehmood, A., Maple, C., Khan, M. A., & Aldegheishem, A. (2020). Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles. IEEE Access, 8, 199618-199628.
3. Giannaros, A., Karras, A., Theodorakopoulos, L., Karras, C., Kranias, P., Schizas, N., ... & Tsolis, D. (2023). Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. Journal of Cybersecurity and Privacy, 3(3), 493-543.
4. Biswas, A., Reon, M. O., Das, P., Tasneem, Z., Muyeen, S. M., Das, S. K., ... & Islam, M. M. (2022). State-of-the-art review on recent advancements on lateral control of autonomous vehicles. IEEE Access, 10, 114759-114786.
5. Boddupalli, S., Rao, A. S., & Ray, S. (2022). Resilient cooperative adaptive cruise control for autonomous vehicles using machine learning. IEEE Transactions on Intelligent Transportation Systems, 23(9), 15655-15672.
6. Dasgupta, S., Rahman, M., Islam, M., & Chowdhury, M. (2022). A sensor fusion-based GNSS spoofing attack detection framework for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(12), 23559-23572.
7. Feroz, B., Mehmood, A., Maryam, H., Zeadally, S., Maple, C., & Shah, M. A. (2021). Vehicle-life interaction in fog-enabled smart connected and autonomous vehicles. IEEE Access, 9, 7402-7420.
8. Abbas, A., Krichen, M., Alroobaea, R., Malebary, S., Tariq, U., & Jalil Piran, M. (2021). An opportunistic data dissemination for autonomous vehicles communication. Soft Computing, 25(18), 11899-11912.
9. Vargas, J., Alsweiss, S., Toker, O., Razdan, R., & Santos, J. (2021). An overview of autonomous vehicles sensors and their vulnerability to weather conditions. Sensors, 21(16), 5397.
10. Hussain, M. I., Azam, S., Rafique, M. A., Sheri, A. M., & Jeon, M. (2022). Drivable region estimation for self-driving vehicles using radar. IEEE Transactions on Vehicular Technology, 71(6), 5971-5982.
11. Xia, L., Sun, Y., Swash, R., Mohjazi, L., Zhang, L., & Imran, M. A. (2022). Smart and secure CAV networks empowered by AI-enabled blockchain: The next frontier for intelligent safe driving assessment. IEEE Network, 36(1), 197-204.
12. Alsulami, A. A., Al-Haija, Q. A., Alturki, B., Alqahtani, A., & Alsini, R. (2023). Security strategy for autonomous vehicle cyber-physical systems using transfer learning. Journal of Cloud Computing, 12(1), 181.
13. Kim, T. H., Ojo, S., Krichen, M., & Alamro, M. A. (2024). Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks. Electronics, 13(10), 1885.
14. Osman, R. A. (2023). Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach. Electronics, 12(19), 4023.
15. Cui, J., Chen, Y., Zhong, H., He, D., Wei, L., Bolodurina, I., & Liu, L. (2023). Lightweight encryption and authentication for controller area network of autonomous vehicles. IEEE Transactions on Vehicular Technology, 72(11), 14756-14770.
16. Khan, M. Z., Sarkar, A., Ghandorh, H., Driss, M., & Boulila, W. (2022). Information fusion in autonomous vehicle using artificial neural group key synchronization. Sensors, 22(4), 1652.
17. Raptis, G. E., Katsini, C., Alexakos, C., Kalogeras, A., & Serpanos, D. (2022). CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning. Applied Sciences, 12(22), 11631.
18. Vitale, C., Piperigkos, N., Laoudias, C., Ellinas, G., Casademont, J., Escrig, J., ... & Khodashenas, P. S. (2021). CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks. EURASIP Journal on Wireless Communications and Networking, 2021, 1-28.
19. Kumar, P., Kumar, R., Jolfaei, A., & Mohammad, N. (2024). An Automated Threat Intelligence Framework for Vehicle Road Cooperation Systems. IEEE Internet of Things Journal.
20. Rathore, M. S., Poongodi, M., Saurabh, P., Lilhore, U. K., Bourouis, S., Alhakami, W., ... & Hamdi, M. (2022). A novel trust-based security and privacy model for internet of vehicles using encryption and steganography. Computers and Electrical Engineering, 102, 108205.
21. Chen, D., & Lv, Z. (2022). Artificial intelligence enabled Digital Twins for training autonomous cars. Internet of Things and Cyber-Physical Systems, 2, 31-41.
22. Girdhar, M., You, Y., Song, T. J., Ghosh, S., & Hong, J. (2022). Post-accident cyberattack event analysis for connected and automated vehicles. IEEE Access, 10, 83176-83194.
23. Wang, L., & Zhang, X. (2023). Anomaly detection for automated vehicles integrating continuous wavelet transform and convolutional neural network. Applied Sciences, 13(9), 5525.
24. Sangapu, S. C., Prasad, K. N., Kannan, R. J., Chen, T. M., & Sathiyanarayanan, M. (2023). Impact of class imbalance in VeReMi dataset for misbehavior detection in autonomous vehicles. Soft Computing, 1-11.
25. Kakkar, R., Gupta, R., Agrawal, S., Tanwar, S., & Sharma, R. (2022). Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G. Journal of Information Security and Applications, 67, 103179.
26. Cao, M., Wang, R., Chen, N., & Wang, J. (2021). A learning-based vehicle trajectory-tracking approach for autonomous vehicles with lidar failure under various lighting conditions. IEEE/ASME transactions on mechatronics, 27(2), 1011-1022.
27. Khatab, E., Onsy, A., & Abouelfarag, A. (2022). Evaluation of 3D vulnerable objects’ detection using a multi-sensors system for autonomous vehicles. Sensors, 22(4), 1663.
28. Muzahid, A. J. M., Kamarulzaman, S. F., Rahman, M. A., & Alenezi, A. H. (2022). Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles. IEEE Access, 10, 43303-43319.
29. Chang, C. C., Ooi, Y. M., & Sieh, B. H. (2021). IoV-based collision avoidance architecture using machine learning prediction. IEEE Access, 9, 115497-115505.
30. Phung, K. H., Tran, H., Nguyen, T., Dao, H. V., Tran-Quang, V., Truong, T. H., ... & Steenhaut, K. (2021). OneVFC—A vehicular fog computation platform for artificial intelligence in Internet of Vehicles. IEEE Access, 9, 117456-117470
31. Driss, M., Almomani, I., e Huma, Z., & Ahmad, J. (2022). A federated learning framework for cyberattack detection in vehicular sensor networks. Complex & Intelligent Systems, 8(5), 4221-4235
32. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
33. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
34. Li, B.; Chen, H.; Lin, X.; Duan, H. Multimodal Learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: A multicenter diagnostic study. Int. J. Surg. 2024, 110, 3237–3248.
35. Zhu, L.; Lai, Y.; Ta, N.; Cheng, L.; Chen, R. Multimodal approach in the diagnosis of urologic malignancies: critical assessment of ChatGPT-4V’s image-reading capabilities. JCO Clin. Cancer Inform. 2024, 8, e2300275.
36. Lin, A.C.; Liu, Z.; Lee, J.; Ranvier, G.F.; Taye, A.; Owen, R.; Matteson, D.S.; Lee, D. Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study. Surgery 2024, 175, 121–127.
37. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
38. Wang, Y.; Chen, W.; Han, X.; Lin, X.; Zhao, H.; Liu, Y.; Zhai, B.; Yuan, J.; You, Q.; Yang, H. Exploring the reasoning abilities of multimodal large language models (mllms): A comprehensive survey on emerging trends in multimodal reasoning. arXiv 2024, arXiv:2401.06805.
39. Panda, R., Dash, S., Padhy, S., & Khandakhani, S. W. (2024). Visualizing and Understanding the Customized Convolutional Neural Networks to Identify Hand Written Odia Characters and its Pattern Using Generative AI.
40. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
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