隨著高齡人口快速增加，高齡駕駛者的「適駕能力」評估已成為日益重要的公共衛生議題；年齡相關的視覺注意力、處理速度與執行功能衰退，皆會提高肇事風險。然而現行評估方式在「安全性」與「直接性」之間難以兼顧：實際道路測試雖具生態效度，卻有風險且難以標準化；而非道路駕駛篩檢雖然安全，卻僅能間接反映真實駕駛行為。結合內建眼動追蹤的虛擬實境（VR）提供了一種兼具控制性與沉浸感的替代方案，能在單一且可重複的任務中，同步捕捉駕駛背後的知覺與動作歷程。
本論文開發一套可同步量測眼動與駕駛行為的VR系統，並以之建立年輕成人（30–39歲）與健康高齡者（65–85歲）之年齡比較基準（初步）。系統以 Meta Quest Pro 頭戴式顯示器（內建 90 Hz 眼動追蹤）、Thrustmaster T150 方向盤與 Unity 引擎建置，施測順向掃視/反向掃視（prosaccade/antisaccade）眼動任務與分級駕駛任務（基礎、雙重任務、互動交通），同步紀錄凝視與駕駛訊號並於 MATLAB 萃取眼動與駕駛行為特徵。組間差異以古典統計（獨立樣本 t 檢定、相關與線性迴歸）分析。此外，本研究亦進行了初步的再測信度評估。
相較於年輕成人，健康高齡者在反向掃視（antisaccade）任務中錯誤率顯著較高（39.3% vs 13.9%，p < 0.001，大效果量），且伴隨較多且較長的凝視與較長的掃視路徑；其掃視潛時（saccade latency）反而較短，本研究將此解讀為抑制控制（inhibitory control）下降，而非處理速度較快。於駕駛行為方面，高齡者平均車速較慢（p < 0.001）。此外，他們在不同的場景下皆表現出更長的凝視時間，這可能反映出其資訊處理時間增加，以及視覺搜尋效率降低。在基礎駕駛場景中，方向盤反轉率（steering-reversal rate）較高（p = 0.03）。在雙任務駕駛場景中，年輕人的違規次數較高（p = 0.04），可能反映出高齡者採取了較為保守的駕駛策略。在互動式交通駕駛場景中，高齡者的「記憶正確率」（memorize correct rate）顯著低於年輕人（兩者分別為 28.6% 與 90.6%；p < 0.001）。值得注意的是，高齡者於互動情境中亦較常闖越紅燈，此一抑制失誤與其反向掃視錯誤相呼應，與反應性抑制控制共同衰退的說法相容，惟本研究設計無法確立此一共同機制。本研究並檢驗認知、眼動與駕駛表現三者間之關聯（error rate × latency r=−0.63、dual fixations × speed r=−0.68，詳見§3.1.5）。結果顯示本平台對跨兩種模態的年齡相關變化具敏感性。此外，在 4 名受試者的子群體中進行的再測信度評估顯示，順向眼動反應時間具備極佳的穩定性（ICC = 0.983，p = 0.001），同時在反向眼動反應時間中也觀察到中等至良好的點估計值（ICC = 0.716，p = 0.087），惟未達統計顯著。這些發現初步支持該平台適用於縱向或重複評估的研究方案。
本研究之貢獻有二：其一為一套整合的 VR 工具，可於單一情境同步施測結構化順向掃視/反向掃視（prosaccade/antisaccade）眼動任務與分級駕駛任務；其二為初步之年齡比較資料，為本平台對年齡相關變化之敏感性提供初步證據。兩者共同奠定本實驗室後續邁向輕度認知障礙（MCI）篩檢與以人工智慧預測適駕能力之方法學基礎（可重複使用之平台、特徵管線與收案準則）。

The rapid growth of the aging population has made the assessment of fitness to drive in older adults an increasingly important public-health concern, particularly as age-related declines in visual attention, processing speed, and executive control elevate crash risk. Existing assessment approaches, however, force a trade-off between safety and directness: on-road evaluation is ecologically valid but risky and poorly standardized, whereas off-road screening tools are safe but only indirectly related to the act of driving. Virtual reality (VR) with integrated eye tracking offers a controlled yet immersive alternative that can capture the perceptual and motor processes underlying driving within a single, reproducible task.
This thesis develops a VR-based system that concurrently measures eye movements and driving behavior, and uses it to establish a preliminary age-comparison baseline contrasting young adults (30–39 years) with healthy older adults (65–85 years). The system, built on a Meta Quest Pro head-mounted display (90 Hz integrated eye tracking), a Thrustmaster T150 steering wheel, and the Unity engine, administers a prosaccade/antisaccade oculomotor task together with a graded driving battery (basic, dual-task, and interactive traffic), and logs synchronized gaze and driving signals that are processed into oculomotor and drivingbehavior features in MATLAB. Group differences were examined using classical statistics (independent-samples t-tests, correlation, and linear regression). Additionally, a preliminary test-retest reliability assessment was conducted.
Relative to young adults, healthy older adults made substantially more antisaccade errors (error rate 39.3% vs 13.9%, p < 0.001, large effect), and during the antisaccade task they produced more and longer fixations and longer scan paths; counter-intuitively, their saccade latencies were shorter, which we interpret as reduced inhibitory control rather than faster processing. In driving behavior, older adults drove more slowly (p < 0.001). Furthermore, they exhibited longer gaze durations across various scenes, potentially reflecting increased information processing time and reduced visual search efficiency. In the basic driving scene, older adults showed a higher steering-reversal rate (p = 0.03). In the dual-task driving scene, young adults committed a higher number of violations (p = 0.04), which may reflect a more conservative driving strategy among the older adults. In the interactive traffic scene, older adults had a significantly lower correct-response rate on the memorization task than young adults (28.6% vs 90.6%, p < 0.001). Notably, older adults also ran more red lights in the interactive scene, a pattern that parallels their antisaccade errors and is compatible with a shared decline in reactive inhibitory control, although the present design cannot establish such a common mechanism. Correlations among cognition, eye movements, and driving performance were also examined (error rate×latency r=−0.63, dual fixations×speed r=−0.68, see §3.1.5 for details). Collectively, these findings suggest that the platform is sensitive to age-related change across both modalities. Additionally, a preliminary test-retest reliability assessment in a subcohort of 4 participants showed excellent stability for prosaccade reaction time (ICC = 0.983, p = 0.001) alongside a moderate-to-good point estimate for antisaccade reaction time that did not reach statistical significance (ICC = 0.716, p = 0.087), providing preliminary support for the platform’s suitability for longitudinal or repeated-assessment protocols.
The contribution of this work is twofold: a working, integrated VR instrument that co-administers a structured prosaccade/antisaccade oculomotor task and a graded driving battery within a single session, and a preliminary age-comparison dataset that provides initial evidence of the platform’s sensitivity to age-related change. Together they provide a methodological foundation — a reusable platform, feature-extraction pipeline, and recruitment protocol — for the laboratory’s longer-term goal of mild cognitive impairment (MCI) screening and AI-based prediction of fitness to drive.

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