Functionality improvement of driverless cars using traffic data and artificial neural networks
محل انتشار: دومین همایش سیستم های حمل و نقل هوشمند جاده ای
سال انتشار: 1395
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 452
فایل این مقاله در 8 صفحه با فرمت PDF و WORD قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
RMTO02_068
تاریخ نمایه سازی: 13 شهریور 1396
چکیده مقاله:
This paper aims to improve the functionality of driverless cars using traffic data based on a follow-up study. Artificial neural networks are used for training a driverless car to react and decide whether to accelerate or decelerate with respect to the speed and acceleration of adjacent vehicles as well as their types and positions to the car. To this end, an Equivalent Vehicle (EV) is introduced and defined for each car in the traffic data and used for training of a neural network. A feed-forward and a time-delay neural network (TDNN) are used to simulate a driver’s reaction. The TDNN tends to show a better performance compared to a feed-forward neural network. This method is illustrated using the NGSIM traffic data.
کلیدواژه ها:
نویسندگان
Seyed Saber Naseralavi
Civil Engineering Department Shahid Bahonar University of Kerman Kerman, Iran
Mohammad Mohammad
Civil Engineering Department Shahid Bahonar University of Kerman Kerman, Iran