دانلود مقاله پیاده سازی خودروی بدون راننده
خلاصه
معرفی
بررسی ادبیات
روش شناسی
الزامات و تجزیه و تحلیل نتایج
نتیجه گیری و دامنه آینده
منابع
Abstract
Introduction
Literature Review
Methodology
Requirements and Result Analysis
Conclusion & Future Scope
References
چکیده
در سال های اخیر تعداد تصادفات جاده ای و شدت آنها افزایش چشمگیری داشته است. این حوادث بیشتر به دلیل خطای انسانی رخ می دهد. برخی از خطاهای رایج انسانی مانند حواس پرتی، رانندگی با خواب آلودگی، رانندگی با سرعت بالا و تاخیر در زمان واکنش رانندگان منجر به تصادفات مرگبار می شود که منجر به تلفات جانی و خسارت مالی می شود. این عوامل را می توان با استفاده از خودروهای خودران از بین برد. خودروی بدون راننده (یا خودروی خودران یا خودروی خودمختار) وسیله نقلیه ای است که با استفاده از حسگرها، دوربین ها، رادار، GPS و هوش مصنوعی (هوش مصنوعی) می تواند با ناوبری خود و بدون دخالت انسان از نقطه ای به نقطه دیگر حرکت کند. ما یک ماشین بدون راننده را با استفاده از Deep Learning، OpenCV، DonkeyCar، TensorFlow و C++/Python پیاده سازی خواهیم کرد.
Abstract
In recent times, there has been a drastic increase in the number of road accidents and their severity. These accidents mostly happen due to human error. Some common human errors like distracted driving, drowsy driving, driving at high speeds and delayed reaction times of the drivers result in fatal accidents that cause loss of life and damage to property. These factors can be eliminated by means of self-driving cars. A driverless car (or self-driving car or autonomous car) is a vehicle that can travel from one point to another by self-navigating without any human intervention by using sensors, cameras, radar, GPS, and AI (Artificial Intelligence). We will be implementing a Driverless Car using Deep Learning, OpenCV, DonkeyCar, TensorFlow and C++/Python.
Introduction
There has been a large spike in the number of automobile accidents these days. According to the U.S. Department of Transportation (USDOT), it has been estimated that about 20,160 people have died in road accidents in the first six months of 2021 in the U.S. alone. In India, data from the National Crime Records Bureau (NCRB) shows that in 2020, an average of 37 people died per 100 car crashes which results in a total of about 1.3 lakh deaths. Also, we need to note that these numbers include the period of lockdown due to the Covid-19 pandemic, during which there was minimal traffic movement.The major causes of these catastrophic accidents include distracted driving, drowsy and drunk driving. These factors lead to increased human error, which in turn increases the risk of having an accident. We can overcome the dangerous consequences of human error using autonomous vehicles or driverless cars. Most leading automobile manufacturers across the globe have started research and development of semi-autonomous and fully autonomous cars.
Conclusion & Future Scope
Driver errors while driving are the most prominent cause of traffic accidents. Driverless cars have the following benefits related to road transport: reduce stress due to driving, reduce driver cost, provide mobility of non-drivers, increased safety, and decrease in traffic congestion. Even a person with disabilities can make use of autonomous vehicles the same as any other individual and can even customize the vehicles for more convenience. A self driving car not only increases safety, but it also improves efficiency and productivity of the people who spend most of their time stuck in traffic and trying to reach their work places. Donkey is a self-driving car platform which uses a high-level Python library to attain autonomous driving. We have trained the Donkeycar to drive on its own based on driving style and path. This uses a supervised learning technique often referred to as behavioral cloning. Next, we carry out steering and throttle calibration. The reason for calibrating the car is to make it drive consistently. Later, the car is driven using a web controller/ physical joystick controller. As the driving car becomes reliable, we used Keras to train a neural network to drive like a human.