自動駕駛系統在實際應用中面臨諸多挑戰，包括光線不足、惡劣天氣，以及複雜的交通情境。如何克服這些挑戰，並實現安全、便利且穩定的自駕技術，是當前重要的課題。物件偵測作為系統中關鍵的技術之一，在感知環境扮演重要的角色，因為準確的物件偵測是保障行車安全的基礎。然而，目前許多偵測方法依賴單一感測器資訊，導致在動態且不可預測的場景中難以穩定運作。為了解決這一問題，基於多模態感測器融合的物件偵測技術被受關注，期待透過不同感測器資訊的整合來提升系統的感知能力。
在本論文中，我們提出了兩個新的多模態感測器融合框架來解決當前在自駕系統中物體偵測技術的問題。第一個框架專注於二維物件偵測任務，透過融合可見光影像及熱影像感測器資訊，來提升系統在不同環境條件下的表現。第二個框架針對三維物件偵測任務，結合影像及點雲模態中的語意與深度資訊，來強化對遠距離、小型及遮擋物件的偵測能力。此外，本研究建置一台實驗車，除了收集真實行駛資料外，也將技術整合並實現於車載平台，以驗證所提出框架的可行性。最後，藉由多個具代表性的公開資料集及實際應用場景測試的全方面實驗證實，所提出的解決方案相較於現有方法具備更高的偵測性能與及時的執行速度，這使其能更有效應對現實駕駛環境中的各種挑戰，為未來更安全及可靠的自動駕駛系統開發奠定基礎。

Autonomous driving systems face numerous challenges in real-world applications, including low-light conditions, adverse weather, and complex traffic scenarios. Overcoming these challenges to achieve safe, convenient, and stable autonomous driving technology is a critical issue. Object detection, one of the key technologies in autonomous systems, plays a crucial role in environmental perception, as accurate detection is essential for ensuring driving safety. However, many current detection methods rely on single-sensor information, making them less stable in dynamic and unpredictable environments. To address this problem, multi-modal sensor fusion for object detection has gained attention, aiming to improve system perception by integrating data from various sensors.
In this dissertation, we propose two new multi-modal sensor fusion frameworks to address current challenges in object detection for autonomous driving systems. The first framework focuses on 2D object detection, enhancing performance under varying environmental conditions by fusing data from visible and thermal sensors. The second framework is designed for 3D object detection, combining semantic and depth information from both image and point cloud modalities to improve detection of distant, small, and occluded objects. Furthermore, we developed an experimental vehicle that not only collected real-world driving data but also integrated and implemented these technologies on an in-vehicle platform to validate the feasibility of the proposed frameworks. Finally, comprehensive experiments conducted on multiple representative public datasets and real-world scenarios demonstrate that the proposed solutions outperform existing methods in both detection accuracy and real-time execution, providing a foundation for the development of safer and more reliable autonomous driving systems in the future.

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