Adaptive AI-Based Multi-Sensor Fusion for Robust ADAS Perception and Real-Time Automotive Safety

  • Authors

    • Pruthvi Kaluvala Embedded Software Engineer Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I4P101

    Published 10-04-2026

  • Advanced Driver Assistance Systems, ADAS, Artificial Intelligence, Deep Learning, Sensor Fusion, Camera, Radar, Lidar, Object Detection, Lane Detection, Collision Detection, Embedded Systems, Autonomous Vehicles, Intelligent Transportation Systems

    Issue

    Vol. 9 No. 4 (2026)

    Section

    Articles

  • Abstract

    Advanced Driver Assistance Systems (ADAS) enhance automotive safety through automated perception, driver warnings, and vehicle-assistance functions. However, camera, radar, and LiDAR sensors have different limitations under varying environmental and traffic conditions. This paper proposes an Adaptive AI-Based Multi-Sensor Fusion (AAMSF) framework that dynamically adjusts sensor contributions based on perception quality, sensor validity, temporal consistency, and environmental conditions. The fused representation supports vehicle and pedestrian detection, lane detection, object tracking, and collision-risk estimation. The framework integrates insights from previous research on CNN-LSTM lane detection, LiDAR-based collision detection, Transformer-based perception, embedded sensor architectures, sensor-fault detection, and driver-state monitoring. Time-to-Collision (TTC) is incorporated for Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB). The framework can be evaluated using KITTI and nuScenes datasets under diverse driving conditions. Performance metrics include precision, recall, F1-score, mAP, IoU, inference latency, frame rate, memory utilization, and collision-risk prediction accuracy. This approach aims to improve perception reliability while maintaining real-time embedded execution for safer autonomous and assisted driving.

  • References

    [1] S. A. M. H. H. M. M. et al., “Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles,” IEEE Consumer Electronics Magazine, vol. 7, no. 5, pp. 18–25, 2018, doi: 10.1109/MCE.2018.2828440.

    [2] S. Y. Chen et al., “Multi-Sensor Fusion in Automated Driving: A Survey,” IEEE Access, vol. 8, pp. 2847–2868, 2019, doi: 10.1109/ACCESS.2019.2962554.

    [3] “Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles,” in Proc. IEEE Intelligent Vehicles Symposium (IV), 2025, doi: 10.1109/IV64158.2025.11097396.

    [4] “A Progressive Review: Emerging Technologies for ADAS Driven Solutions,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 326–341, 2022, doi: 10.1109/TIV.2021.3122898.

    [5] “A Systematic Review on Sensor Fusion Technology in Autonomous Vehicles,” in Proc. 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), 2023, doi: 10.1109/ICESC57686.2023.10193038.

    [6] A. Geiger, P. Lenz, and R. Urtasun, “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, doi: 10.1109/CVPR.2012.6248074.

    [7] H. Caesar et al., “nuScenes: A Multimodal Dataset for Autonomous Driving,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

    [8] “Multi-Sensor Fusion and Cooperative Perception for Autonomous Driving: A Review,” IEEE Intelligent Transportation Systems Magazine, vol. 15, no. 5, pp. 36–58, 2023, doi: 10.1109/MITS.2023.3283864.

    [9] O. Gurrapu, “Real-Time Lane Detection and Tracking Using Hybrid CNN-LSTM Models for Smart Transportation,” in Proc. 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS), IEEE, 2026.

    [10] V. Painuly, O. Gurrapu, W. H. Jebaselvi, U. Abdalov, Y. Noushad, and V. C. Gandhi, “AI-Enhanced Collision Detection for Autonomous Drones Using LiDAR and Neural Network,” in Proc. 2025 Second International Conference on Networks and Soft Computing (ICNSoC), IEEE, 2025.

    [11] S. Saravanan, B. P. Deepak Kumar, S. Ganga, Y. Jeevan Nagendra Kumar, H. Muhammed Alii, and O. Gurrapu, “Transformers for Multi-Scale Deep Learning in Real-Time Visual Perception Systems,” in Proc. 2025 International Conference on Metaverse and Current Trends in Computing (ICMCTC), IEEE, 2025.

    [12] J. Venkata Suman, O. Gurrapu, A. Ananthi Christy, Aenikapati Swetha Priya, Mirjalol Ismoilov Ruziboy Ugli, and Sardor Sabirov, “FPGA-based Embedded Sensor Architecture for High-Speed Data Acquisition Systems,” in Proc. 2025 International Conference on Modern Sustainable Systems (CMSS), IEEE, 2025.

    [13] O. Gurrapu, Sudha Rajesh, Raxmanov Jurabek, V. Parthiban, T. Chandra Sekhar Rao, P. Sivasankar Rajamani, J. Venkata Suman, and B. Venkataramanaiah, “Design and Implementation of Selective Adaptive Pipeline Structure for Low Power RISC Processor Applications,” in Proc. 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), IEEE, 2025.

    [14] O. Gurrapu and J. Venkata Suman, “A Machine Learning Framework for Fault Detection in IoT Enabled Smart Sensor Networks,” in Proc. 2025 Global Conference on Information Technology and Communication Networks (GITCON), IEEE, 2025.

    [15] J. Venkata Suman, O. Gurrapu, Badana Govinda Rajulu, Hayitov Abdulla Nurmatovich, Anorgul Ashirova, Yuldashev Shadiyor, and P. Kalyan Chakravarthi, “Real-Time EEG-Based Drowsiness Detection Using Deep Learning Algorithms,” in Proc. 2025 7th International Conference on Energy, Power and Environment (ICEPE), IEEE, 2025.

    [16] K. Sambath Kumar, O. Gurrapu, J. Prabhakaran, Ch. Bhavani, D. Madhavi Latha, and Lakkakula Kavya, “Real-Time Driver Drowsiness Detection System using IoT-based Physiological Monitoring and Web Interface,” in Proc. 2025 8th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, 2025.

    [17] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision Meets Robotics: The KITTI Dataset,” International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.

    [18] “High-Performance Automotive Radar: A Review of Signal Processing Algorithms and Modulation Schemes,” IEEE Signal Processing Magazine, vol. 36, no. 5, pp. 32–44, 2019, doi: 10.1109/MSP.2019.2911722.

    [19] “Advanced Driver Assistance System (ADAS) in Autonomous Vehicles: A Complete Analysis,” in Proc. 2023 8th International Conference on Communication and Electronics Systems (ICCES), 2023, doi: 10.1109/ICCES57224.2023.10192617.

  • Downloads

Similar Articles

91-100 of 102

You may also start an advanced similarity search for this article.