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  • Open Access

    ARTICLE

    Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task

    Yang Su1,*, Xianrang Shi1, Tinglun Song2

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036 - 19 May 2025

    Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

  • Open Access

    ARTICLE

    VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

    Haeri Kim1, Jong-Moon Chung1,2,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3589-3605, 2022, DOI:10.32604/cmc.2022.023073 - 07 December 2021

    Abstract The development of Vehicular Ad-hoc Network (VANET) technology is helping Intelligent Transportation System (ITS) services to become a reality. Vehicles can use VANETs to communicate safety messages on the road (while driving) and can inform their location and share road condition information in real-time. However, intentional and unintentional (e.g., packet/frame collision) wireless signal jamming can occur, which will degrade the quality of communication over the channel, preventing the reception of safety messages, and thereby posing a safety hazard to the vehicle's passengers. In this paper, VANET jamming detection applying Support Vector Machine (SVM) machine learning… More >

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