隨著自動駕駛技術的進步，高度自動化駕駛的汽車很可能在未來十年內進入市場，也被預期會產生許多正向的效果，例如提昇道路安全和減少燃料消耗；然而，自駕車必須被社會大眾廣泛接受才能真正發揮這些效益。影響使用者對於自動駕駛汽車及其技術的接受因素包含駕駛人個人差異、系統可靠性及駕駛人對系統的信任，其中信任是影響大眾對此新技術之使用意願的前提。過去研究建議於人機互動設計中加入擬人化特徵以及提供適當的回饋可提高信任，因此本研究將針對自動駕駛汽車之駕駛權交遞的過程，以上述兩種方案為基礎，探討如何改善人機互動設計，進而讓駕駛人對自動駕駛汽車產生適當的信任；然而目前市場上僅有少數能實際上路且擁有自動駕駛系統的車輛，故本研究利用Unity 3D模擬行駛於平面市區道路的情境，藉由沉浸式虛擬實境模擬使用者與自動駕駛汽車的互動。
本研究共招募28位（男性22位、女性6位；內控性格者8位、外控性格者20位）年齡介於20至30歲間、雙眼裸視視力0.8以上或戴隱形眼鏡矯正後視力0.8以上、持有汽車駕照、具兩年以上駕駛經驗且過去半年內每週至少駕駛一次之參與者，並應用駕駛憤怒量表初步將可能過於保守或冒險之駕駛人排除。由於駕駛權的交遞過程包含前段之「自動駕駛」和後段之「駕駛人接手駕駛」，因此分為兩個實驗：第一部分欲探討有助於建立最適信任程度的擬人化自動駕駛行為，第二部分則整合第一部分所發現的最佳自動駕駛行為，針對駕駛權交遞之過程探討有助於建立最適信任程度的駕駛權接手提醒時間點。第一部分實驗中，研究參與者必須以隨機順序體驗四種自動駕駛行為（激進、保守、混合、重現駕駛人自身行為），並於每個實驗情境結束後填寫評估自覺有用性、自覺易用性、使用意圖、信任和不信任等的量表，藉由主觀感受比較其間的差異，以找出最有助於駕駛人對自駕車建立信任的駕駛行為；第二部分實驗中，研究參與者會先體驗自動駕駛，此期間於儀表板及方向盤右側播放影片讓參與者分心，但不強制要求參與者全程專注觀看，然後隨機在不同時間點（自駕系統自行運作極限前6.4秒、7.6秒、8.8秒、10.0秒）的情境下以視、聽覺提醒接手駕駛權，接手後需透過駕駛模擬器完成轉彎、避開道路封閉路段並停車的駕駛任務，於每個實驗情境結束後填寫評估自覺有用性、自覺易用性、使用意圖、信任和不信任等的量表以及情境察覺評分技術量表（SART），並結合駕駛權接手駕駛權的反應時間、任務成敗（包含是否確實打方向燈以及是否在不發生碰撞的情況下轉彎、避開道路封閉路段）兩項客觀績效指標，以評估駕駛人對自駕車的信任。
整體而言，本研究發現自駕車的人機互動設計確實為影響駕駛人對自駕車信任與使用與否的關鍵因素，因此自駕系統的自動駕駛行為之設定宜排除「激進型」自動駕駛行為，並針對駕駛人的「車速」、「車速變化」、「與前車車距」、「在路口前的加減速的距離」、「打方向燈的時機」、「轉彎的順暢與穩定度」等六種駕駛行為特徵配合駕駛人的駕駛風格，而系統極限前8.8秒是駕駛人較信任且駕駛績效較佳的駕駛權接手提醒時間點，基於安全性的考量，設計時宜排除系統極限前6.4秒的提醒時間點。此外，針對自動駕駛行為設計的評估可忽略駕駛性格和性別的影響，但應考量駕駛人的個別習慣差異，而針對駕駛權接手提醒時間點設定的評估，則需考量駕駛性別的影響，以協助提昇駕駛人對自駕車的信任。

While the autonomous vehicle technology progresses, highly automated vehicles are likely to enter the market within the next decade. There are many positive impacts, such as enhancing road safety and reducing fuel consumption. However, these effects can only arise if autonomous vehicles are accepted by the society. The acceptance of autonomous vehicles can be affected by the individual differences among drivers, system reliability, and drivers’ trust towards the system. Trust towards autonomous vehicles are the premises in the process of acceptance formation. In order to enhance trust, the implementation of anthropomorphic features has been suggested, as well as providing proper feedback in human-machine interaction (HMI). Therefore, this study focuses on the take-over process and explores how to improve the MI design to help users build appropriate trust towards autonomous vehicles. However, there are only few highly autonomous vehicles on the market. So, the immersive virtual reality system was used with Unity 3D to simulate driving scenarios.
28 participants (22 males, 6 females; 8 internal locus of control, 20 external locus of control) that are between the age of 20 and 35, holding the driver’s licenses and with over 2 years of driving experience were recruited. Besides, those who are too conservative or too risky were excluded by using the Driving Anger Scale. Since the process of take-over consists of “autonomous driving” and “the driver taking over the control,” the experiment is divided into two parts. The first part is to explore what kind of self-driving behavior can help the driver to build the appropriate trust. The second part will integrate the best driving behavior identified in the first experiment to find out the best timing of take-over request for drivers to build the appropriate trust. In the first experiment, the participant was asked to experience four self-driving behaviors (aggressive, conservative, hybrid, driver-like). After completion of each experimental scenario, the participant completed a series of scales to assess perceived usefulness, perceived ease of use, intention to use autonomous vehicles, trust and distrust, in order to find out the self-driving behavior that is most helpful for the driver to build trust towards autonomous vehicles. In the second part of the experiment, each participant had to first experience the autonomous driving mode with the best behavior identified in the first experiment. A video clip was played in-car to distract the driver, but the participant was not forced to watch it with full attention all the time. Furthermore, the participant was asked to take over the control and complete a driving task (turning to avoid the front vehicle and road closure) by using the driving simulator at different timing of reminder (6.4, 7.6, 8.8, 10.0 seconds before the system limit). The participant was asked to fill in the same scales as in the first experiment and the situation awareness rating technique (SART) scale after each experimental scenario. Combining the results of subjective indicators and two objective performance indicators (reaction time and success rate of the manual driving task), the driver's trust toward autonomous vehicles were evaluated.
In general, the HMI design of autonomous vehicles was found to be a key factor that affects the driver’s trust and intention of use towards autonomous vehicles. Therefore, “aggressive” self-driving behavior should be excluded. Besides, the seld-driving behavior should be set to match the driving style of the driver in terms of "speed," "speed change," "distance from the vehicle ahead," "distance to facilitae acceleration/deceleration at the intersection," "timing of turning on the turn signal," and "smoothness and stability of turning." Drivers tend to have a higher level of trust towards the reminder given 8.8 seconds prior to the system limit, which also contributes to the better driving performance. Based on safety considerations, the reminder given 6.4 seconds prior to the system limit should be excluded. In addition, when conducting the evaluation of self-driving behavior design, the influence of the driver’s personality and gender can be ignored. Instead, the self-driving behavior should be improved according to the driver’s individual habits. As for conducting the evaluation of the timing of the reminder for take-over, it is suggested to consider the driver’s gender in order to help improve the driver’s trust towards self-driving vehicles.

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