智能車載是現今被廣為討論的議題，如何增加行車安全同時又能節省汽油消耗是智能車載發展的重要挑戰之一。為了達到增加行車安全之目的，車載系統(On-Board Unit, OBU)必須具備感知複雜環境資訊的能力進而偵測出車道中潛在的危險，因此我們建立了一個多感測器資料融合系統用以處理複雜的環境資訊。根據文獻[20]中提出之結論，在一般的交通狀況下，車道之車流量最大化可以降低整體車道之行車油量消耗，因此我們開發了變換車道機制，當駕駛被前車阻擋而無法通過紅綠燈時，建議駕駛變換車道。此一機制之目的為藉由減緩交通壅塞來達到車流量最大化，進而節省整體車道之行車油量消耗。
系統所需要之感測資料來源包含慣性感測元件、超聲波感測器、雷達感測器、速度感測器、攝影機、路側單元(Road Side Unit, RSU)之封包、OBD-II訊息以及GPS。當執行變換車道時，為了能夠保持安全距離，我們使用卡爾曼濾波器將特定的重要參數做資料融合，使之達到穩定。除了感知自車周圍環境外，評估目前所在之車道狀況也是一樣重要的工作，因此我們基於貝氏分類法開發了一個新穎的車道狀況評估方式，並且建立了一個決策演算法用於判斷當下狀況是否建議變換車道。
最後，我們實現了可感知環境資訊的資料融合平台，並可透過使用者介面顯示變換車道之建議。在實驗結果方面，我們分析且討論了特定參數融合之結果以及車道狀況評估之結果。此外，系統整體反應時間(從存取原始資料到顯示建議結果)大約花了0.3秒，符合了我們的預期。

The issues of intelligent vehicles are now widely discussed. Increasing road traffic safety and at the same time reducing fuel consumption is one of the most challenging future tasks. In order to improve road traffic safety, the in-vehicle system (On-Board Unit, OBU) must have the ability to perceive complex environment information and detect potential threats on the road. For that reason, we create a multi-sensor data fusion system to process environment information. According to the paper in [20], with maximized traffic throughput, the global fuel consumption will be improved in normal traffic conditions. We develop a lane-changing mechanism that can make a lane-changing decision if there is a forward vehicle blocked the way we go forward, and then we could not pass through the traffic light in front. The purpose of lane-changing mechanism is to maximize traffic throughput for achieving global fuel saving by decreasing vehicles jammed on the road.
The data sources include IMU (Inertial Measurement Unit), ultrasound sensor, lidar, speed sensor, camera, RSU (Road Side Unit), OBD-II (On-Board Diagnostics) and GPS. We use Kalman filter to stabilize significant parameters for adjusting safe distance while performing lane-change. In addition to perceive surrounding of ego vehicle, the traffic situation assessment of current road is also an important task. So we develop a novel traffic situation assessment method based on Bayesian classification and a decision algorithm to decide whether the occasion is appropriate to change lane or not.
Finally, we built the fusion system with user interface, which can perform the recommendation of lane-change to the driver. In the experiment results, we analyzed and discussed about the performance of specific parameters estimation and traffic situation assessment. Moreover, the system’s response time (from the procedure of accessing raw data to the procedure of showing the lane-changing result) is approximately 0.3 second that measure up to our expectation.

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