Abstract
For autonomous vehicles and intelligent connected vehicles, the real-time recognition of risky drivers can play an important role in traffic accident prevention. However, the external environment substantially impacts driving behavior and driving risk and is usually costly to acquire. Existing risky driver recognition models often ignore external environment information or assume this information is given. We propose two hierarchical two-layer context-aware machine learning structures. The first layer can speculate external context, for example, traffic states. The second layer recognizes risky drivers based on the contextual information speculated from the first layer. The German Highway Drone Dataset is used to establish risky driver recognition and traffic state recognition models. Rear-end collision risk and side collision risk are evaluated for each vehicle. Drivers with high collision risk are labeled as risky drivers. By analyzing vehicle trajectory data from three traffic states: free-flow, saturated, and congested, we find that traffic states have a significant influence on vehicle’s longitudinal speed, lateral speed, longitudinal acceleration/deceleration, and collision risk. Six classifiers, including SVM, KNN, RF, Adaboost, Extra trees, and XGBoost, are applied to train recognition models. Results show that the proposed structures can significantly improve model’s ability to recognize risky drivers.
Highlights
New Information and Communication Technologies (ICT) are redefining the automobile industry
Risky driver labeling, and driving behavior parameters was done on Matlab 2018b
We believe that traffic state is an essential factor in risky driver recognition, and the relationship between traffic state and collision risk is complicated that congestion does not always cause traffic risk
Summary
New Information and Communication Technologies (ICT) are redefining the automobile industry. Identifying an individual’s driving style and dangerous driving behavior can enhance AV and ICV’s ability to perceive risk from the interaction with surrounding vehicles, adjust driving strategy to driving conditions, and improve road traffic safety. Marina et al [3] stated that driving style is strongly influenced by external driving conditions, like traffic, road type, time in the day, weather, the same driver could switch driving style under different driving conditions. The driving style studies in most papers could be either relatively permanent or changing with external driving conditions, depending on the measurement methods of the driving style chosen by researchers. Aggressive driving is one of the heavily studied driving styles, and it can be measured by self-report questionnaires and observations of driving behavior. The measured driving style could be influenced by the driving condition under which the driving behavior data was observed. For many studies that rely on driving behavior data collected from naturalistic driving experiments, driving simulators, and vehicle trajectory data, the external driving
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