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.

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