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  • Open Access

    ARTICLE

    Multi-Modal Attention Networks for Driving Style-Aware Trajectory Prediction in Autonomous Driving

    Lang Ding, Qinmu Wu*, Jiaheng Li, Tao Hong, Linqing Bian

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1999-2020, 2025, DOI:10.32604/cmc.2025.066423 - 29 August 2025

    Abstract Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style… More >

  • Open Access

    ARTICLE

    Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

    Nidhi Kalra1,*, Raman Kumar Goyal1, Anshu Parashar1, Jaskirat Singh1, Gagan Singla2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1967-1978, 2021, DOI:10.32604/cmc.2021.018732 - 21 July 2021

    Abstract Every 24 seconds, someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years. 1.35 million people globally die every year due to road traffic crashes. An additional 20–50 million suffer from non-fatal injuries, often resulting in long-term disabilities. This costs around 3% of Gross Domestic Product to most countries, and it is a considerable economic loss. The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents. However, there… More >

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