車輛間可見光通訊之影像訊號辨識優化與應用

Abstract

近年來人工智慧與機器學習的技術發展十分蓬勃，隨著AI在各方面取得顯著的成功，過去認知的許多需要人工操作的領域都漸漸地能被電腦取代。雖然目前自動駕駛的汽車還不普及，但隨著技術不斷突破，自動駕駛取代手動駕駛勢必成為未來主流。 車間通訊是自動駕駛的一個重要的環節，透過與鄰近車輛的通訊可以即時獲得附近車輛的位置及速度，進一步判斷是否有撞擊的風險、需不需要加減速。在這個領域中可見光通訊是常常被應用且具有潛力的，其一是因為現在的車燈大多都採用LED燈，LED的發光效率好且調變快速，非常適合拿來作為可見光通訊的發送端；其二是可見光通訊的易遮蔽特性和指向性，使得車輛在路上可以排除不必要的雜訊，只接收鄰近車輛特定指向的訊號。 本篇論文用Arduino開發板來調變一組LED裝置，並用手機相機對LED燈攝影來進行可見光通訊，以這樣的裝置來模擬車燈對行車紀錄器的鏡頭通訊的情況。本篇論文著重於利用捲簾快門效應和影像處理的技巧讓程式自動辨識車燈光源和一般光源的不同，再將處理後的影像進一步做解碼以及邊界檢測。其中邊界檢測除了尋找解碼範圍外，亦可以用來判讀車燈大小，進一步推算與前車距離。 在影像處理的部份主要用到自適應的直方圖等化及二維卷積來強化識別經過編碼的車燈影像的能力。訊號編碼的部份採用開關鍵控（on-off keying，OOK）調變，並加入遊程長度限制碼（run-length limited，RLL）來確保不會因為連續的0影響照明和程式判讀訊號的能力。受限於相機的每秒影格數，本裝置每秒僅能傳送約16位元，目前設計來傳送車輛時速的資訊是相當夠用的。

Artificial Intelligence (A.I.) and technologies of machine learning had made significant success in recent years. Many of things that are known as “human-operating-only” can be done well by A.I. Although self-driving cars are not widespread now, it’s can be anticipated that self-driving will become more popular as the technology is improved in the future. Vehicle-to-vehicle (V2V) communication is one of the important parts of self-driving. The positions and velocities of nearby cars can be detected by V2V communication, and the information can be further used to prevent collisions. Visible light communication (VLC) has high potential in this field. One of the reasons is that most of cars are using light-emitting-diodes (LEDs) for illumination and LEDs are easily modulated and have high efficiency, which is a perfect transmitter for VLC. The other reason is that visible light can be blocked easily and has high directivity. So that only nearby signals generated in proper orientations are detected, which is an important benefit in V2V communication. In this thesis, an Arduino board was used to modulate arrays of LEDs, and use smart phone camera as the receiver to build a VLC system. This device is used to simulate the actual situation of VLC between the tail-light of a car and a dash cam. In this thesis, rolling-shutter effect and image processing are used to identify difference between normal light source and the modulated tail-light of car. After this, boundary detection is used to decode message and estimate the distance between cars. For image processing, adaptive the histogram equalization and the two-dimensional convolution are used to strengthen the ability of the system to identifying modulated tail-light. On-off keying is used for modulation, and run-length limited (RLL) coding prevents too many continuous zeros affecting illumination and decoding signal. In the end, limited by the frame rate of camera, this device can only send 16 bits per second, which is designed to transmit velocity information of car.