College of Transportation Engineering, Tongji University, Shanghai 201804, China.
Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University, Shanghai 201804, China.
Int J Environ Res Public Health. 2020 Mar 31;17(7):2375. doi: 10.3390/ijerph17072375.
Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. First, a surrogate measurement of collision risk, called Average Crash Risk (ACR), is proposed to calculate a vehicle's crash risk. Second, the driver's driving aggressiveness is determined by his/her ACR with three anomaly detection methods. Third, we train classification models to identify aggressive drivers using partial trajectory data. Three imbalanced class boosting algorithms, SMOTEBoost, RUSBoost, and CUSBoost, are compared with cost-sensitive AdaBoost and cost-sensitive XGBoost. Additionally, we try two resampling techniques with AdaBoost and XGBoost. Among all algorithms tested, CUSBoost achieves the highest or the second-highest Area Under Precision-Recall Curve (AUPRC) in most datasets. We find the discrete Fourier coefficients of gap as the key feature to identify aggressive drivers.
实时识别危险驾驶行为和攻击性驾驶员是一个很有前途的研究领域，这要归功于强大的机器学习算法和车载及路边传感器提供的大数据。然而，由于攻击性驾驶员在实际交通中的出现频率较低，大多数机器学习算法对每个样本一视同仁，更倾向于更好地预测正常驾驶员，而不是攻击性驾驶员，这是我们真正感兴趣的。本文旨在利用车辆轨迹数据测试不平衡类提升算法在攻击性驾驶员识别中的优势。首先，提出了一种称为平均碰撞风险（ACR）的碰撞风险替代度量方法来计算车辆的碰撞风险。其次，通过三种异常检测方法，用驾驶员的 ACR 来确定驾驶员的驾驶攻击性。然后，我们使用部分轨迹数据训练分类模型来识别攻击性驾驶员。比较了三种不平衡类提升算法（SMOTEBoost、RUSBoost 和 CUSBoost）与基于代价敏感的 AdaBoost 和 XGBoost。此外，我们还尝试了两种基于 AdaBoost 和 XGBoost 的重采样技术。在所有测试的算法中，CUSBoost 在大多数数据集的精度召回曲线下面积（AUPRC）中达到最高或第二高。我们发现间隙的离散傅里叶系数是识别攻击性驾驶员的关键特征。
