It is a widely accepted view that considering the memory effects of historical information (driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However, many commonly used models (e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range (ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM (ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

一种广泛接受的观点是，考虑历史信息（驾驶操作）的记忆效应有利于车辆轨迹预测模型提高预测精度。但是，许多常用模型（例如，长期短期记忆，LSTM）只能隐式地模拟记忆效应，但缺乏有效的机制来从序列数据中捕获记忆效应并估计其有效时间范围（ETR）。这种短缺使得很难根据当前的驾驶行为动态配置最合适的历史信息长度，这会损害对汽车运动的良好理解。为了解决这个问题，我们提出了一种基于有序神经元LSTM（ON-LSTM）的改进的轨迹预测模型。我们证明了基于ON-LSTM进行ETR估算的可行性，并提出了一种ETR估算方法。我们估算NGSIM I-80数据集上的驾驶波动和换道操作的ETR。实验结果证明，该方法能够很好地捕捉轨迹预测过程中的记忆效应。此外，估计的ETR值与我们的直觉相符。