The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

联网自动驾驶汽车（CAV）的出现将改变驾驶行为和出行环境，并为更安全，更顺畅和更智能的道路运输提供机会。在从当前的人类驾驶汽车（HDV）过渡到完全CAV交通环境的过程中，道路交通将包含HDV和CAV的“混合”交通流。配备有多个传感器和车辆之间的通信，CAV可以跟踪周围的HDV并接收通信范围内其他CAV的轨迹数据。这些轨迹数据可以利用深度学习方法的最新进展来潜在地预测目标HDV的轨迹。基于这些预测，CAV可以做出反应来规避或减轻交通流量的波动和事故。这项研究开发了基于注意力的长期短期记忆（LSTM）模型，用于混合流环境中的HDV纵向轨迹预测。该模型和其他一些LSTM变体在具有不同CAV市场渗透率（MPR）的新一代Simulation US 101数据集中进行了测试。结果表明，即使MPR低至0.2，利用周围CAV的历史轨迹的LSTM模型的性能也比忽略信息的LSTM模型好得多。基于注意力的LSTM模型可以提供更准确的多步纵向轨迹预测。此外，进行网格级别的平均注意力权重分析，并确定对目标HDV未来轨迹有较大影响的CAV。该模型和其他一些LSTM变体在具有不同CAV市场渗透率（MPR）的新一代Simulation US 101数据集中进行了测试。结果表明，即使MPR低至0.2，利用周围CAV的历史轨迹的LSTM模型的性能也比忽略信息的LSTM模型好得多。基于注意力的LSTM模型可以提供更准确的多步纵向轨迹预测。此外，进行网格级别的平均注意力权重分析，并确定对目标HDV未来轨迹有较大影响的CAV。该模型和其他一些LSTM变体在具有不同CAV市场渗透率（MPR）的新一代Simulation US 101数据集中进行了测试。结果表明，即使MPR低至0.2，利用周围CAV的历史轨迹的LSTM模型的性能也比忽略信息的LSTM模型好得多。基于注意力的LSTM模型可以提供更准确的多步纵向轨迹预测。此外，进行网格级别的平均注意力权重分析，并确定对目标HDV未来轨迹有较大影响的CAV。结果表明，即使MPR低至0.2，利用周围CAV的历史轨迹的LSTM模型的性能也比忽略信息的LSTM模型好得多。基于注意力的LSTM模型可以提供更准确的多步纵向轨迹预测。此外，进行网格级别的平均注意力权重分析，并确定对目标HDV未来轨迹有较大影响的CAV。结果表明，即使MPR低至0.2，利用周围CAV的历史轨迹的LSTM模型的性能也比忽略信息的LSTM模型好得多。基于注意力的LSTM模型可以提供更准确的多步纵向轨迹预测。此外，进行网格级别的平均注意力权重分析，并确定对目标HDV未来轨迹有较大影响的CAV。