In modern traffic management, the ability to recognize different vehicle types and count the number of vehicles quickly and accurately is required to ensure road safety and efficient management. Thus, we constructed a machine learning model by combining Doppler radar technology with Long Short-Term Memory (LSTM) neural networks for the real-time analysis and accurate recognition of traffic dynamics. Doppler radar technology is advantageous in vehicle monitoring as it effectively captures changes in vehicle speed and distance, and depicts the dynamic characteristics of the vehicle. Combined with the LSTM model, the constructed model processed and analyzed the time series data. It learned the movement patterns of the vehicle to identify potentially hazardous vehicles and provide timely warnings to avoid traffic accidents. At the same time, by optimizing the traffic signal control, the LSTM neural network was used to identify and recognize the vehicle dynamics in real time. At the same time, the efficiency of road traffic was improved by optimizing traffic signal control. The model was used for traffic flow monitoring, accident prevention, traffic congestion management, and so on. The model can be used in various traffic environments providing stable and reliable vehicle identification, which is significanct in improving the safety and efficiency of road traffic.


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    Title :

    Intelligent Traffic Control and Accident Prevention with Sensor-Assisted Doppler Radar-based Vehicle Identification


    Contributors:


    Publication date :

    2024-01-26


    Size :

    593526 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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