论文标题
在线轨迹预测的第一印象时，驾驶风格识别
Driving Style Recognition at First Impression for Online Trajectory Prediction
论文作者
论文摘要
本文提出了一种新的驾驶风格识别方法，该方法允许自动驾驶汽车（AV）对周围车辆的轨迹进行轨迹预测，并具有最小的数据。为此，我们在拟议的方法中使用离线和在线方法的混合物。我们首先在离线部分中使用PCA和K-均值算法学习典型的驾驶方式。之后，局部最大样本技术用于执行在线驾驶样式识别。我们根据预测轨迹的RMSE值和在5S持续时间内观察到的轨迹对其他方法进行了实际驾驶数据集的基准测试。与使用其他文献的参数相比，所提出的方法可以将轨迹预测误差最多减少37.7％，而与未执行驾驶样式识别相比，最多可将轨迹预测误差降低到24.4 \％。
This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7\% compared to using the parameters from other literature and up to 24.4\% compared to not performing driving style recognition.
