對於提供駕駛者注意以及提升駕駛流暢度，辯識鄰近車輛的相對位置的有效性是十分重要的，然而大部分的無線定位技術，無法提供足夠的定位精準度去辨識車輛相對位置而且需要透過昂貴的硬體設備才能達到高精度。為了解決這個問題，在這篇論文中，我們提出EV車輛相對位置辯識。為了有效率的配對無線訊號以及視覺訊號，EV配對演算法可以做到將在車輛樣貌以及它的特定無線識別之間的配對的可能性提升最大限度。為了評估EV車輛相對位置辯識的可行性，我們實作在Raspberry Pi自走車上。實驗結果顯示出，EV車輛相對位置辯識可以達到很優良的精確度而配對演算法有效率而且趨於穩定。

The availability of relative location of nearby vehicles is critical in providing safety alerts to the drivers and enhancing driving experience. However, most of wireless localization techniques either fail to provide sufficient accuracy to identify the relative vehicle positioning or require expensive hardware to achieve high accuracy. To resolve this issue, in this thesis we propose E-V relative vehicle positioning. To effectively pair electronic and visual signals, the E-V matching algorithm is used, which can maximize the probability of correct pairing between an vehicle's electronic identity and its visual appearance. To evaluate performance of the E-V relative vehicle positioning, a prototype is built on the Raspberry B+ self-driven car. The conducted experiment results show that E-V relative vehicle positioning system is able to achieve a much better vehicle relative positioning accuracy and the matching result is efficient and stable throughout the experiment.

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