توسعه شاخص ایمنی جایگزین ترکیبی برای تصادفات جلو به عقب با استفاده از سیستم استنتاج فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، بخش مهندسی عمران، دانشگاه شهید باهنر کرمان، کرمان، ایران

2 استادیار، بخش مهندسی عمران، دانشگاه شهید باهنر‌ کرمان، کرمان، ایران

3 دانش آموخته کارشناسی ارشد، دانشگاه شهید باهنر کرمان، کرمان، ایران

چکیده

 این مقاله در نظر دارد تا روشی جهت ثبت و شناسایی به‌موقع موقعیت‎های خطرناک برای هر وسیله‌نقلیه براساس مشخصات خرد جریان ترافیک ارائه کند. در اینجا از شاخص­های ایمنی جایگزین تصادفات استفاده می­شود. چنانچه بتوان از ویژگیهای شاخص­های شاخص­های مختلف استفاده کرد، کارایی عملیات پیش­بینی خطر افزایش پیدا خواهد کرد. برای این منظور، در این مقاله از سیستم استنتاج فازی (FIS) جهت ارائه یک شاخص ترکیبی (CSSM) استفاده می­شود. جهت جلوگیری از پیچیده شدن مسئله تنها برخوردهای جلوبهعقب در نظر گرفته شده است.  جهت تعیین مشخصات سیستم استنتاج فازی از داده­های واقعی جمع­آوری شده در بخشی از بزرگراه مدرس در تهران استفاده می­شود. در نهایت نتایج مربوط به تحلیل ایمنی برمبنای هر یک از شاخص­ها با هم و نیز نتایج حاصل از شاخص CSSM به لحاظ آماری با یکدیگر مقایسه می­شوند. براساس محاسبات صورت گرفته می­توان گفت استفاده از FIS می­تواند احتمال برخورد جلوبهعقب را با در نظر گرفتن اثر توأمان شاخص­های مختلف بهتر مدل کند. نتایج این مقاله می­ تواند در بهبود عملکرد خودروهای خودرران موثر باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of applying Fuzzy Inference System to increase the efficiency of intelligent vehicles by Surrogate Safety Measures

نویسندگان [English]

  • Navid Nadimi 1
  • Seyed Saber Naseralavi 2
  • Amirhossein Zare mirhosseini 3
1 Assistant Professor, Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
2 Assistant Professor, Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
3 M.Sc., Grad., Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
چکیده [English]

This paper aims to develop a method to detect dangerous situations for each vehicle based on microscopic traffic data for intelligent vehicles. Here, surrogate safety measures (SSMs) would be applied. Each SSM has unique characteristics and if we could use the advantages of different SSM simultaneously, then the efficiency of intelligent vehicles might be increased. For this purpose, Fuzzy Inference System (FIS) is applied to develop a Combined Surrogate Safety Measure (CSSM). In order to avoid complicating the issue, only rear-end collisions are considered. Microscopic traffic data collected in Modares highway of Tehran is used to develop FIS. Finally, the CSSM results are compared by each SSM statistically. Based on the results, it can be declared that FIS can be helpful to calculate the rear-end collision probability by using different SSMs. This achievement can be useful in promoting the efficiency of autonomous vehicles. 
 
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کلیدواژه‌ها [English]

  • safety
  • Autonomous Vehicle
  • Rear-end collision
  • Fuzzy
  • surrogate safety measure
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