過去十年，隨著科技的快速發展，自駕車研究成為全世界關注的重要課題，全球的知名車廠包含Audi、Benz、Honda、Toyota、Volvo等等，針對自駕車的研發都傾盡全力，希望可以在未來的市場佔有一席之地，由此可見自駕車成為未來交通領域的主力技術已不再是遙不可及的空談。在未來，自駕車的研發與其正式上路還是有許多需要被考慮的問題，其中最重要的不外乎安全性的問題、便利性的問題以及實際上路後交通車流的問題。
在過去，車流模型包含了智慧駕駛模型（IDM）、跟車模型、自適應巡航控制模型（ACC）等等。交通車流模型中會考慮到的變數包含車輛的速度、兩車間的間距、安全時間間距等等。雖然在過去的研究中有眾多的車流相關模型，但其中只有少數的研究考慮到自動駕駛汽車、車道變換以及市區道路的應用。此外，在這些交通車流模型之間也是有許多差異的，其中包含了巨觀與微觀的差異、考慮單車道或是多車道的差異。這些不同的觀察指標、不同的環境設置也都會影響交通車流模型的發展。
本研究的目的在開發一種新的多車道自動駕駛汽車的車流模型，也希望可以利用此模型進行模擬路口號誌之情形。在這個自駕車交通車流模型中，我們主要會考慮車輛的速度、加減速以及車跟車之間的間距。本研究除了會利用python建立一個模擬程式外，也會利用SUMO的模擬結果進行比較，而由於自適應巡航控制模型（ACC）並不考慮反應時間，因此利用SUMO觀察不同反應時間對人駕車跟車行為的影響，本研究將會利用開發出來的程式，加入本研究所設計的車道變換規則模擬高速公路雙車道的情境，接著再加入本研究所設計之號誌規則模擬市區道路路口的自駕車車流狀況。最後，希望本研究的多車道自駕車車流模型將會為相關當局提供有關自駕車車流的一些建議和參考。

In the past decade, the rapid development of technology had brought a lot of effects to our lives. Autonomous vehicles had become a serious topic for the world. Some world-renowned brands such as Audi, Benz, Honda, Tesla, Toyota, Volvo, etc., had already spent much time and money doing a lot of research about autonomous vehicles. From the resources spent by these vehicle brands, the autonomous vehicles get on the road is not the dream anymore. However, when autonomous vehicles get on the road, that will cause a lot of safety problems, convenience problems, and traffic flow problems.
In the past, the traffic flow models involved an intelligent driver model (IDM), car-following model, and Adaptive cruise control model (ACC), etc. As we can see that the parameters of traffic flow models had thought about including the velocity of vehicles, the distance between the leading and the following vehicles, and the safety time gap between vehicles, etc. Although there were many traffic flow models in the past research, there had few models that considered the problems of multilane autonomous vehicle traffic flow included autonomous vehicles, lane-changing rules, and the applications of the urban intersection. Therefore, there are differences between these traffic flow models which including not only the concepts of macroscopic or microscopic but also single-lane or multilane. These differences bring influence the development of traffic flow models.
This research aims to develop multilane autonomous vehicles traffic flow models that consider autonomous vehicles (AVs), lane-changing rules, and use this model to simulate the traffic conditions of the urban intersection. In this research, we focus on the parameters that involving vehicle speed, acceleration, deceleration, and the distance between vehicles. This research uses Python to construct the simulation program and compares the results with the SUMO simulation. Due to the ACC do not consider the reaction time, this research uses SUMO to observe the different reaction times of the human-driven car-following behavior. This research uses the program to combine the lane-changing rules we design to simulate the freeway traffic condition. Therefore, this research adds the signal system into the program above to simulate the traffic flow situation of the urban intersection. Finally, this research is expected to provide some recommendations and references about autonomous traffic flow for related authorities.

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