自駕車(Autonomous Vehicles, AVs)因其便利之特性，近年來發展快速，使用者急速增加。自駕車因有導航與物件偵測等先進技術，普遍認為可降低人為失誤事故、提升道路交通安全、最小化運行成本、提升道路容積與改善交通壅塞。本研究致力於探討其影響自駕車乘客傷亡之關鍵因素。然而，現有自駕車仍不時有傷亡事故發生，一旦自駕車正式上路運行，自動駕駛的不確定因素可能造成災難性的影響。目前，自駕車仍為小規模測試階段，碰撞和傷亡事故資料較少，故其事故集仍為小樣本事件，且資料集具類別分布不均的特性。此外，僅有少數研究定義了影響碰撞嚴重程度的環境因素，例如碰撞地點周圍的設施和道路相關資訊。這些研究在選擇空間特徵時往往忽略了小樣本量和不平衡資料的問題。
本研究使用加州機動車輛管理局 (California Department of Motor Vehicles, CA DMV) 提供之三年（2019-2021）的自動駕駛汽車碰撞報告（AV Level 3）。 該數據集包含 266 份碰撞報告，其中包含 51 份傷亡案件和 215 份無傷亡案件。 除了自駕車碰撞報共中所提供之相關資訊外，碰撞地點周圍環境也是影響自駕車碰撞嚴重程度的重要因素。 因此，本研究從開放街道地圖 (Open Street Map, OSM) 和 DataSF 蒐集多項設施及道路限速資料，建立興趣點 (Point of Interests, POIs) 數據集,以在微觀尺度上精確評估碰撞與周圍環境之間相互作用的影響。
為了解決上述小樣本和數據集不平衡的問題，本研究提出使用兩種方法：隨機過採樣示例（ROSE）和合成少數過採樣技術（SMOTE）。 ROSE 和 SMOTE 皆為常見的過採樣方法。ROSE 方法採用平滑引導在稀缺樣本周圍的特徵空間內生成新樣本；而SMOTE 為基於ROSE的優化方法，結合ROSE的優點與K最近鄰 (K Nearest Neighbors, KNN) 演算法以生成新樣本。SMOTE所產生的新合成樣本由原始樣本與其鄰近樣本組成之線段上的隨機位置所決定。本研究通過採用這些平衡方法，改善不平衡類別的分佈並擴增數據集。
此外，儘管現有研究已經採用多種特徵選擇方法來定義自駕車碰撞事故的影響因子，但仍然缺乏不同分類器之間的性能比較。本研究採用三種不同類型的特徵選擇方法，包含互信息(Mutual Information, MI)、隨機森林(Random Forest, RF)和極限梯度提升(eXtreme Gradient Boosting, XGboost)，並比較其特徵選擇的成果。另外，本研究也針對特徵選擇及樣本平衡執行程序的影響進行探討，並設計了兩種不同的實驗程序以進行分析比較。
本研究成果顯示與嚴重 AV 碰撞相關的關鍵變量包含製造商、損壞程度、碰撞類型、運動狀態、衝突方和特定 POI。顯著的 POIs 影響因素包含道路資訊（速限）、交通運輸設施（共享汽車租借站和汽車充電站）、娛樂場所（宗教場所和夜店）、公共場所（公共設施）、教育場所（學校）、醫療設施（牙醫診所）等。實驗結果證明了過採樣方法和不同實驗流程的性能差異，可改善自駕車碰撞資料集小樣本及類別分布不均的問題。本研究成果指出：(1)過採樣方法有助於提升自駕車碰撞傷亡與否的預測能力，其中以SMOTE表現最佳(MODEL 9)。(2)特徵選擇前先進行過採樣的研究程序有助於選擇出更具代表性的解釋變量。(3)優化的演算法不一定為最有效的特徵選擇方法；因此，使用者應依據資料集之特徵選擇合適的分類器。最後，本研究成果有助於促進自駕車正式上路運行，也可以應用於台灣的操作設計領域 (Operation Design Domain, ODD)。本研究選擇之變數可納入未來自駕車場景測試之關鍵參數設計。

Autonomous vehicles (AVs) have gained significant popularity recently due to their convenience. It is believed that the potential advantages of AVs can reduce human error crashes, enhance traffic safety, minimize the operational costs, increased road capacity, and improve road congestion. This study specifically focuses on identifying the influencing factor of AV collision, which typically result in few deaths and injuries. However, the potential ramifications for the future could be catastrophic when AVs start to operate on the road. Currently, AVs are still undergoing small-scale testing, making collisions and casualties rare occurrences. Moreover, only few studies have thoroughly defined the environmental factors, such as surrounding environment and road information, that contribute to the different severity of these collisions. Additionally, these studies often overlook the challenges posed by small sample sizes and imbalanced datasets when selecting spatial features.
The study dataset included three years (2019-2021) of AV collision reports (AV Level 3) from California Department of Motor Vehicles (CA DMV). The dataset consisted of 266 collision reports, comprising 51 injured cases and 215 non-injured cases. In addition to the AV collision-related information, the study also considered surrounding environmental factors as important influencing factors for AV collision severity. Therefore, multiple facilities and road speed limits data were collected from Open Street Map (OSM) and DataSF to create the point of interests (POIs) dataset. This allowed for a precise assessment of the influence of the interaction between the collision and the surrounding environment at a micro scale.
To address the aforementioned issues of small sample size and imbalanced dataset, this study proposes the utilization of two methods: Random Over-Sampling Examples (ROSE) and Synthetic Minority Over-Sampling Technique (SMOTE). ROSE and SMOTE are commonly employed oversampling approaches that aim to tackle these challenges. The ROSE employs bootstrapping method to generate new samples within the feature space surrounding the minority class. On the other hand, SMOTE is an optimized technique that combines the benefits of random over sampling with K Nearest Neighbors (KNN) algorithm to create artificial samples. The new synthetic instance is created in the line segment between the given raw sample and its nearest neighbors in the feature space. By employing these balancing methods, both the distribution of the imbalanced class as well as the overall dataset are improved and expanded.
In addition, despite the utilization of various feature selection approaches to address this issue, there is still a lack of performance comparison among different classifiers. This study aims to fill this gap by conducting three different types of feature selection methods (mutual information, random forest, and XGboost) and comparing their results. Furthermore, the study also investigates the impact of the procedure involving feature selection and balancing. Two distinct experimental procedures were designed to analyze in this topic.
The key variables related to injured AV collisions included manufacturers, damage level, collision type, movement state, conflict parties and specific POIs. The significant POIs encompassed road information (speed limit), transportation (car sharing, and charging stations), entertainment (places of worship, and nightclubs), public place (social facilities), education (schools), medical facility (dentists). Furthermore, the experimental results demonstrated the superior performance of the balancing methods and procedure. The findings in this study demonstrate that: (1) Oversampling method helps to improve the predictive ability and SMOTE performs best (MODEL 9). (2) The experimental procedure that balancing before feature selection can select the most representative influencing factors. (3) The integrated algorithm may not always be the most effective, thereby, user should choose the classifier based on the characteristics of the dataset. Finally, this study contributes the enhancement for AV operation on the public roads and can also be applied to the Operational Design Domain (ODD) in Taiwan. The selected variables can be the key factor to design the AV testing in the future.

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