پیش‌بینی تغییر خط راننده در آزادراه‌ها با استفاده از شبکه عصبی بازگشتی با حافظه بلندمدت و ماشین بردار پشتیبان بر اساس داده‌های میکروسکوپی

نوع مقاله : علمی - پژوهشی

نویسندگان
1 گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 استاد، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران، ایران
3 استادیار، گروه مهندسی عمران، واحد کرج، دانشگاه آزاد اسلامی، تهران، ایران
4 استادیار، گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
تغییر خط یکی از مانورهای حیاتی در رانندگی است که نقش مهمی در ایمنی و روانی جریان ترافیک دارد. هدف این پژوهش، توسعه و مقایسه مدل‌های یادگیری ماشین برای پیش‌بینی کوتاه‌مدت تغییر خط رانندگان در آزادراه است. بدین منظور، از داده‌های میکروسکوپی مجموعه‌داده شبیه‌سازی نسل آینده (NGSIM) استفاده شد و دو مدل شامل شبکه عصبی بازگشتی با حافظه بلندمدت (LSTM) و ماشین بردار پشتیبان (SVM) طراحی و ارزیابی گردیدند. پیش‌بینی‌ها در بازه‌های زمانی ۱ تا ۵ ثانیه پیش از وقوع مانور انجام شد تا اثر افق زمانی بر عملکرد مدل‌ها بررسی شود. برای ارزیابی، معیارهایی نظیر میانگین مربع خطا (MSE)، بازخوانی (Recall) و امتیاز F1 مورد استفاده قرار گرفتند. نتایج نشان داد مدل LSTM در تمامی شاخص‌ها نسبت به SVM برتری معناداری دارد و در بازه‌های کوتاه‌مدت (۱ تا 5 ثانیه) به بالاترین دقت و کمترین خطا دست یافت. همچنین تحلیل آماری نشان داد که در خطوط راست آزادراه تمایل به تغییر خط بیشتر است. یافته‌های این پژوهش می‌تواند در طراحی سیستم‌های کمک‌راننده پیشرفته و توسعه الگوریتم‌های پیش‌بینی دقیق در خودروهای نیمه‌خودران و خودران مورد استفاده قرار گیرد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Driver Lane-Change Prediction on Freeways Using Long Short-Term Memory Network and Support Vector Machines Based on Microscopic Data

نویسندگان English

Mohammad Maniat 1
Mahmoud Saffarzadeh 2
hasan zoghi 3
سرکار Sarkar 4
1 Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran
2 Professor,Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
3 Assistant Professor, Department of Civil Engineering, Islamic Azad University, Karaj, Iran
4 Assistant Professor, Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran
چکیده English

Lane changing is one of the most critical driving maneuvers, playing a significant role in both traffic safety and flow efficiency. The aim of this study is to develop and compare machine learning models for short-term prediction of drivers’ lane-change behavior on freeways. For this purpose, high-resolution microscopic data from the Next Generation Simulation (NGSIM) dataset were utilized, and two predictive models—Long Short-Term Memory (LSTM) neural networks and Support Vector Machines (SVM), were designed and evaluated. Predictions were performed for time horizons ranging from 1 to 5 seconds prior to the lane-change maneuver in order to assess the effect of prediction horizon on model performance. Evaluation metrics, including Mean Squared Error (MSE), Recall, and F1-score, were applied to assess model accuracy. The results indicate that the LSTM model significantly outperformed SVM across all evaluation criteria and achieved the highest accuracy with the lowest error in short-term horizons (1–5 seconds). Furthermore, statistical analysis revealed that lane-change maneuvers were more frequent in the right lanes of the freeway. The findings of this study can contribute to the design of advanced driver-assistance systems (ADAS) and the development of accurate predictive algorithms for semi-autonomous and autonomous vehicles.

کلیدواژه‌ها English

Lane Change Prediction
LSTM Neural Network
Driver Assistance System
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دوره 17، شماره 4 - شماره پیاپی 69
تابستان 1405
صفحه 5645-5661

  • تاریخ دریافت 22 تیر 1404
  • تاریخ بازنگری 19 مهر 1404
  • تاریخ پذیرش 12 آبان 1404