人工智能和物聯網的進步使得自動駕駛汽車的商用化越來越有可能。隨著自駕車的普及，當前的交通問題，如油耗、擁堵、和高事故率都能夠得到解決。自治交叉口管理（AIM）就是一個例子，利用自動駕駛汽車的高精準度來提高自動駕駛汽車在單一交叉口的效率。然而，在一個由多個相互關聯的交叉口組成的系統中，只是通過改進單個交叉口並不能達到系統的最佳化。因此，我們延伸單個交叉口到複數路口的場景，並提出一種新的車輛繞線方法。此方法結合了自駕車的特性和傳統的繞線演算法來實作自駕車的路徑規劃，並且通過模擬未來的路況來智能地避免擁塞從而實現系統最優化。

Advancements in artificial intelligence and Internet of Things indicates the realization of commercial autonomous vehicles is almost ready. With autonomous vehicles comes new approaches in solving some of the current traffic problems such as fuel consumption, congestion, and high incident rates. \textit{Autonomous Intersection Management} (AIM)\cite{dresner2008multiagent} is an example that utilizes the unique attributes of autonomous vehicles to improve the efficiency of a single intersection. However, in a system of interconnected intersections, just by improving individual intersections does not guarantee a system optimum. Therefore, we extend from a single intersection to a grid of intersections and propose a novel vehicle routing method for autonomous vehicles that can effectively reduce the travel time of each vehicle. With dedicated short range communications and the fine-grained control of autonomous vehicles, we are able to apply wire routing algorithms with modified constraints to vehicle routing. Our method intelligently avoids congestions by simulating the future traffic and thereby achieving a system optimum.

Contents
Acknowledgement i
Abstract ii
1 Introduction 1
1.1 Current State of Transportation System . . . . . . . . . . . . . . . . . . . . 1
1.2 Solutions for the Congestion Problem . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Intelligent Intersection Control . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Autonomous Vehicle Routing . . . . . . . . . . . . . . . . . . . . 3
1.3 Conventional Vehicle Routing . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Background 5
2.1 Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Dynamic Trac Assignment (DTA) . . . . . . . . . . . . . . . . . . . . . 6
3 Related Work 7
3.1 Autonomous Intersection Management (AIM) . . . . . . . . . . . . . . . . 8
3.2 Multiple Intersection Optimization . . . . . . . . . . . . . . . . . . . . . . 10
4 Proposed Methodology 12
4.1 Overall Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.1 Batch Processing Stage . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.2 Routing Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1.3 Congestion Checking Stage . . . . . . . . . . . . . . . . . . . . . 20
5 Experimental Evaluation 22
5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6 Conclusion 27
References 29

[1] K. Dresner and P. Stone, “A multiagent approach to autonomous intersection management,”
Journal of artificial intelligence research, vol. 31, pp. 591–656, 2008.
[2] S. Peeta and A. K. Ziliaskopoulos, “Foundations of dynamic trac assignment: The
past, the present and the future,” Networks and spatial economics, vol. 1, no. 3-4,
pp. 233–265, 2001.
[3] D. K. Merchant and G. L. Nemhauser, “A model and an algorithm for the dynamic
trac assignment problems,” Transportation science, vol. 12, no. 3, pp. 183–199,
1978.
[4] M. O. Sayin, C.-W. Lin, S. Shiraishi, J. Shen, and T. Bas¸ar, “Information-driven autonomous
intersection control via incentive compatible mechanisms,” IEEE Transactions
on Intelligent Transportation Systems, no. 99, pp. 1–13, 2018.
[5] M. O. Sayin, C.-W. Lin, S. Shiraishi, and T. Bas¸ar, “Reliable intersection control in
non-cooperative environments,” arXiv preprint arXiv:1802.08138, 2018.
[6] M. N. Mladenovic and M. M. Abbas, “Self-organizing control framework for driverless
vehicles,” in Intelligent Transportation Systems-(ITSC), 2013 16th International
IEEE Conference on, pp. 2076–2081, IEEE, 2013.
[7] C. Katrakazas, M. Quddus, W.-H. Chen, and L. Deka, “Real-time motion planning
methods for autonomous on-road driving: State-of-the-art and future research directions,”
Transportation Research Part C: Emerging Technologies, vol. 60, pp. 416–
442, 2015.
[8] B. Zheng, C. Lin, H. Liang, S. Shiraishi, W. Li, and Q. Zhu, “Delay-aware design,
analysis and verification of intelligent intersection management,” IEEE International
Conference on Smart Computing (SMARTCOMP), pp. 1–8, 2017.
[9] R. Zhang, F. Rossi, and M. Pavone, “Routing autonomous vehicles in congested transportation
networks: Structural properties and coordination algorithms,” arXiv preprint
arXiv:1603.00939, 2016.
[10] F. Rossi, R. Iglesias, R. Zhang, and M. Pavone, “Congestion-aware randomized
routing in autonomous mobility-on-demand systems,” Extended version available at
https://asl. stanford. edu/wpcontent/papercite-data/pdf/Rossi. Iglesias. Zhang. Pavone.
CDC17. pdf, 2017.
[11] J.-S. Mun, “Trac performance models for dynamic trac assignment: an assessment
of existing models,” Transport reviews, vol. 27, no. 2, pp. 231–249, 2007.
[12] M. Hausknecht, T.-C. Au, and P. Stone, “Autonomous intersection management:
Multi-intersection optimization,” in Intelligent Robots and Systems (IROS), 2011
IEEE/RSJ International Conference on, pp. 4581–4586, IEEE, 2011.
[13] P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination
of minimum cost paths,” IEEE transactions on Systems Science and Cybernetics,
vol. 4, no. 2, pp. 100–107, 1968.
[14] M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, “Sumo–simulation of urban
mobility,” in The Third International Conference on Advances in System Simulation
(SIMUL 2011), Barcelona, Spain, vol. 42, 2011.