In recent years, combining visualization and language models has opened many possibilities. Control and Intelligent Traffic Investigation (ITS) is a very useful application. This article examines the role and importance of integrating visual and linguistic models to increase the efficiency and safety of traffic management and improve traffic outcomes. Integrating vision and language models has been proven to increase the performance and safety of autonomous vehicles and improve traffic and transportation. Perception and decision processes in autonomous vehicles. Leveraging the power of both methods, these models help gain a deeper understanding of the environment by interpreting multimodal information such as images, videos, and text documents. Thanks to image recognition, object recognition and natural language understanding, these machines can understand traffic conditions, road signs, pedestrian behavior, technology, and contextual information, enabling safe and reliable autonomous navigation. In addition, the use of visual language models includes a wide range of intelligent traffic in addition to the intelligent car. By analyzing and interpreting various streams of data from various sources, including data collected from cameras, sensors and city information systems, social media and databases, these models help with real-time traffic management, congestion forecasting and monitoring and optimization methods. Their ability to transmit information from different components allows vehicles to respond to changing conditions, improving safety and efficiency. This article provides an overview of the current advances, approaches and challenges in the development and application of visual modeling in the automotive industry and intelligent transportation systems. Explores the integration of computer vision and powerful language processing techniques and discusses their impact on improving comprehension, decision making, and overall performance. The need to be strong, meaningful, and able to improve the nature of driving and traffic capital according to principles is also important in future guidance and research. In short, it can be stated that the fusion model of vision and language has a lot of promise in the development of driving and intelligent driving. These models combine the understanding and interpretation of multimodal information, resulting in safer, more efficient, and more flexible transportation, bringing us closer to a future of intelligent and more connected people. Finally, we discuss issues and research gaps in depth paper the goal is to provide researchers with current work and the future Trends in VLM in AD and ITS.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Synergistic Fusion: Vision-Language Models in Advancing Autonomous Driving and Intelligent Transportation Systems


    Additional title:

    Inf. Syst. Eng. Manag.




    Publication date :

    2025-01-19


    Size :

    17 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Advancing Autonomous Driving with Large Language Models: Integration and Impact

    Ananthajothi, K / Satyaa Sudarshan, G S / Saran, J U | IEEE | 2024


    SYSTEMS AND METHODS FOR VISION-LANGUAGE PLANNING (VLP) FOUNDATION MODELS FOR AUTONOMOUS DRIVING

    PAN CHENBIN / YAMAN BURHANEDDIN / NESTI TOMMASO et al. | European Patent Office | 2025

    Free access


    Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving

    Zhang, Enming / Gong, Peizhe / Dai, Xingyuan et al. | ArXiv | 2025

    Free access