在本篇論文中，我們主要探討如何讓自動駕駛汽車在現代複雜環境下行駛。目前，能夠在道路上行駛的自動駕駛車輛幾乎都是在相對簡單的道路環境中。為了使自動駕駛車輛能夠安全地在相對複雜的道路環境中行駛，需要升級物件偵測和辨識技術以實現這一目標。 過去，在這個領域中大多數技術都是由高強度的卷積神經網絡(CNN)為主導。然而，近年來隨著技術的進步，許多研究人員逐漸將原本處理自然語言 (NLP)技術的方法應用於這個領域，以獲得更出色的成果。有鑒於此，我們提出了一種結合平行殘差雙融合特徵金字塔網路和自注意力機制的物件模型，來實現模擬車輛行進中的交通燈號辨識。 在我們提出的架構中，我們使用主流的一階段物體偵測模型的骨幹，採用多尺度特徵融合金字塔方法和不同的注意力機制模塊，結合架構調整和優化器的選擇。實驗結果顯示，所提出的方法在所有驗證指標中都有顯著的提升。這表明提出的方法在交通燈偵測和辨識方面確實取得了更好的效果。 ;In this thesis, we mainly discuss how to make the self-driving car driving under complicated environments in modern era. Currently, the self-driving vehicles that can drive on the road are almost in relatively simple road environment. In order to make the self-driving driving safely in the relatively complex road environment, the object detection and recognition technologies need to be upgraded to achieve this goal. In the past, most techniques employed in this field were dominated by high-intensity Convolutional Neural Networks (CNN). However, many researchers have gradually applied the original method of processing Natural Language Processing (NLP) technique in this field to achieve better results with the progress of technology recently. In view of this, we propose an object model by combining parallel residual bi-fusion feature pyramid network and self-attention mechanism to realize traffic light recognition in simulated vehicle maneuvering. In our proposed architecture, we use the backbone of mainstream one-stage object detection model with a multi-scale feature fusion pyramid approach and different attention mechanism modules, coupling with architectural tuning and optimizer selection. Experimental results reveal that the proposed method exhibits noticeable improvement in all verification indicators. It indicates that the proposed method really possesses better results on traffic light detection and recognition.
