论文标题
基于时间卷积网络的改变车道的预测方法
A Lane-Changing Prediction Method Based on Temporal Convolution Network
论文作者
论文摘要
改变车道是一种重要的驾驶行为，不合理的车道变化可能会导致潜在的危险交通碰撞。高级驾驶员援助系统（ADA）可以帮助驾驶员安全有效地更换车道。为了捕获改变道路行为的随机时间序列，本研究提出了一个时间卷积网络（TCN）来预测长期改变车道的轨迹和行为。此外，卷积神经网络（CNN）和复发神经网络（RNN）方法被认为是证明TCN学习能力的基准模型。改变车道的数据集是由驾驶模拟器收集的。 TCN的预测性能是从三个方面展示的：不同的输入变量，不同的输入维度和不同的驾驶场景。预测结果表明，与两个基准模型相比，TCN可以准确地预测长期改变车道的轨迹和驱动行为。 TCN可以提供准确的车道改变预测，这是开发准确ADA的关键信息。
Lane-changing is an important driving behavior and unreasonable lane changes can result in potentially dangerous traffic collisions. Advanced Driver Assistance System (ADAS) can assist drivers to change lanes safely and efficiently. To capture the stochastic time series of lane-changing behavior, this study proposes a temporal convolutional network (TCN) to predict the long-term lane-changing trajectory and behavior. In addition, the convolutional neural network (CNN) and recurrent neural network (RNN) methods are considered as the benchmark models to demonstrate the learning ability of the TCN. The lane-changing dataset was collected by the driving simulator. The prediction performance of TCN is demonstrated from three aspects: different input variables, different input dimensions and different driving scenarios. Prediction results show that the TCN can accurately predict the long-term lane-changing trajectory and driving behavior with shorter computational time compared with two benchmark models. The TCN can provide accurate lane-changing prediction, which is one key information for the development of accurate ADAS.
