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Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This i...
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Context 1
... the RF model performs slightly better than MLP, according to the f1-score of Table 5. As shown in the ROC curve of Random Forest classifier in Figure 5, the model seems to have high ability (approximately 90%), to distinguish between positive class and negative class for all three classes (i.e., safety levels). However, as found in the literature review [53], the interpretation of the ROC curves can be misleading especially in imbalanced classification problems. ...
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Citations
... The design and architecture of the neural network, including the number of layers, neurons, and activation functions, are essential considerations in achieving accurate and effective classification of risky driving behavior. Previous studies [4,14] have explored the application of multi-layer perceptron ANNs in similar contexts, highlighting the network's ability to capture complex patterns and associations in driving data. ...
... The results shown in are in line with relevant literature on real-time safety evaluations [1], as well as previous project analyses utilized on simulator data [4]. Precision and f1-score metrics are probably lower due to the greater amount of 'normal' STZ level instances as compared with 'dangerous' conditions. ...
The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.
... Similarly, with the help of data mining techniques such as decision tree, Naive Bayes, and artificial neural network (ANN), other kinematic data such as gear position and wheel suspensions from CAN (Controller Area Network) bus can also be utilized to classify driving environments according to [8]. More recently, one noticeable method is proposed in [21], where the objective is to estimate the driving behavior and crash risk from onboard vehicle data such as speed, travel distance, and hand-on-wheel event. To achieve that, a variety of multiclass classifiers are investigated, such as Support Vector Machine (SVM), Random Forest, AdaBoost, and Multilayer Perceptron (MLP). ...
To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle’s vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.
Background
Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity.
Methods
This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes.
Results
Findings indicated that the final modified model fitted the data accurately with X32= 16.09, P < .001, X2/ degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators.
Conclusions
The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.
