在自動化高速公路系統中，車輛列隊行駛可以增加交通吞吐量和能源使用效率。協同式自適應巡航控制系統允許車輛之間相互通信和車輛列隊行駛。在車輛列隊行駛時，其中一輛車會以固定的距離或固定的時間間隔跟隨前面的另一輛車。目前大多數研究都假設車輛列隊行駛中所有車輛的類型和能力是一樣的，而且兩個車輛之間的距離是固定的距離。但是，車輛之間的距離應該與車輛能力有關。在這篇論文中，我們考慮不同車輛之間的煞車能力係數，通過重新排序使車輛列隊行駛的總長度最小化。因此，我們制定車輛序列的重新排序問題和提出啟發式演算法來解決此問題，同時嘗試最小化總體的需要時間。

In Automated Highway System (AHS), grouping vehicles into platoons can increase
the trac throughput and energy eciency. With Cooperative Adaptive Cruise Control
(CACC) system, vehicles are allowed to communicated and cooperate with each other to
form platoons, where one vehicle follows another with a predefined spacing or time gap.
Most studies assumed that type and capabilities of all the vehicles in a platoon are homogeneous
and the spacing between two vehicles is a constant spacing. However, spacing
between vehicles should be related to vehicles capabilities. In this thesis, we take into
account braking factor of dierent vehicles in order to minimize platoon length by reordering
platoon sequence. Therefore, we formulate platoon sequence reordering problem and
propose a heuristic algorithm while minimizing the total operation time.

Acknowledgement i
Abstract ii
1 Introduction 1
1.1 Benefits of Vehicle Platooning . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Adaptive Cruise Control (ACC) and Vehicle-to-Vehicle (V2V) . . . . . . . 2
1.3 Cooperative Adaptive Cruise Control (CACC) . . . . . . . . . . . . . . . . 2
1.4 Spacing Policy Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Related Work 5
2.1 Platoon Maneuvers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Sequence Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Preliminaries 10
3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Sequence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Change Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4 Move Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Problem Formulation 15
4.1 Permutation Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Methodology 18
5.1 Enumeration Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.2 Clique-based Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.3 Partition-Merge Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6 Experimental Results 27
6.1 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.2 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7 Conclusion 30
References 31

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