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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Automotive Engineering

    Background:

    • Laser jamming poses a significant threat to automotive cameras, particularly with the rise of light detection and ranging (LiDAR) systems.
    • LiDAR deployment increases risks of detector saturation and image distortion in camera systems.

    Purpose of the Study:

    • To propose and validate an anti-laser-jamming imaging strategy for automotive cameras.
    • To ensure continuous and reliable image acquisition despite laser interference.

    Main Methods:

    • Developed an anti-laser-jamming imaging strategy based on correlated double sampling (CDS).
    • Implemented an alternating dual-channel sampling mechanism within the camera system.
    • Verified the strategy through laboratory simulations, mechanical LiDAR jamming, and real-vehicle road tests using a short-wave infrared (SWIR) camera.

    Main Results:

    • The CDS-based strategy effectively suppressed LiDAR-induced disturbances.
    • Stable and high-fidelity imaging performance was achieved even under laser jamming conditions.
    • The camera remained operational, acquiring complete image frames during jamming events.

    Conclusions:

    • The proposed CDS strategy offers a practical and scalable solution for enhancing automotive imaging sensor resilience against laser jamming.
    • This technology has broad applications in autonomous driving, surveillance, and security systems.
    • The method ensures the reliable operation of cameras in environments with potential laser interference.