# RTACompensator: Leveraging AraBERT and XGBoost for Automated Road Accident Compensation
## 3 Related Work  
Studies in the field of road accidents have examined various aspects, including risk factors [19; 24; 25; 26; 27; 28; 29; 30; 31], which fall into three main categories: human, environmental, and vehicle-related. Research has also focused on crash risk prediction [8; 9; 10; 32; 33; 11; 12], crash anticipation [13; 14; 15; 16; 17; 18], and crash detection using driving recorders [4; 5; 6; 7], as well as surveillance camera videos [34; 35; 33; 36]. Additionally, studies have explored the application of the internet of things [37], deep learning (DL) [38; 39; 39], and ML techniques [40; 41; 42; 43] to enhance crash detection and prevention. Determining human responsibility in crash investigations presents significant challenges. [PERSON] et al. [44] proposed a novel approach focusing on car crashes at intersections, particularly those involving traffic lights. Their method uses a simulation program to perform detailed assessments of crash scenarios. Another approach [45] employs a probability theory-based classifier, specifically a rule-based system utilizing function approximation to capture the uncertainty present in expert knowledge, especially with incomplete data. Applied to the 100-car naturalistic driving dataset, this method highlights the difficulties of making informed decisions based on expert analysis. [PERSON] et al. [46] developed an expert system for processing auto-insurance claims, integrating neural networks and fuzzy logic techniques. This system enhances the accuracy of compensation calculations by using a rule-based framework. Additionally, research in [19] employed a data-driven approach guided by explicit rules to estimate responsibility. The authors compared multiple statistical learning methods, such as logistic regression (LR) with L1 regularization and random forests (RFs), to predict responsibility attributions made by experts based on routinely recorded police data. Furthermore, the authors of [1] developed a multiagent system designed to predict each actor's responsibility in road accidents, particularly at crossroads. This system uses driving recorder video footage as its input data source and outputs predictions regarding the responsibility of the actors involved in the accident. The study in [47] investigates RTA severity in the UK by integrating ML, econometric techniques, and time series forecasting. It employs regression analysis using the generalized method of moments (GMM) to improve precision. Time series models, such as vector autoregressive (VAR) and autoregressive integrated moving average (ARIMA), are applied for accurate forecasting. Additionally, ML models, including RF and optimized XGBoost, are implemented through H2O Automated ML, achieving high predictive performance. Figure 5: RTACompensator workflow. The research in [48] focuses on improving the safety assessment of automated driving systems by predicting RTA types to generate test scenarios. Using German police accident data, the authors estimate globally recognized three-digit accident types, which describe accident-causing conflicts often missing in raw data. A classification model was developed to predict 30 types of turning accidents. The categorical boosting (CatBoost) model outperformed BERT when leveraging accident descriptions and non-text features, such as collision type. The research in [49] addresses the issue of road traffic injuries, a leading global cause of mortality, by proposing the Stacked DCL-X model to classify the \"victim_degree_of_injury\" in RTAs. Using the California-based SWITRS dataset, which includes 127000 accidents, the authors predict injury severity resulting from vehicle collisions with pedestrians, obstacles, and other vehicles. The Stacked DCL-X model integrates multiple ML classifiers, including XGBoost, CatBoost, RF, and light gradient boosting machine (LightGBM), combined with the Harris hawk optimization algorithm for feature selection.