Title: Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios Show Chinese title

Title: 基于深度学习的车辆速度预测在城市和高速公路场景下的生态自适应巡航控制

Abstract: In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. To control a host vehicle in an energy-efficient manner using model predictive control (MPC), and moreover, enhance the performance of an ecological adaptive cruise control (EACC) strategy, forecasting the future velocities of a target vehicle is essential. For this purpose, a deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. Besides these, the physics-based constant velocity (CV) and constant acceleration (CA) models are discussed. The sequential time series data for training (e.g. speed trajectories of the target and its preceding vehicles obtained through vehicle-to-vehicle (V2V) communication, road speed limits, traffic light current and future phases collected using vehicle-to-infrastructure (V2I) communication) is gathered from both urban and highway networks created in the microscopic traffic simulator SUMO. The proposed speed prediction models are evaluated for long-term predictions (up to 10 s) of target vehicle future velocities. Moreover, the results revealed that the LSTM-based speed predictor outperformed other models in terms of achieving better prediction accuracy on unseen test datasets, and thereby showcasing better generalization ability. Furthermore, the performance of EACC-equipped host car on the predicted velocities is evaluated, and its energy-saving benefits for different prediction horizons are presented. Show Chinese abstract

Abstract: 在典型的车车跟随场景中，目标车辆的速度波动作为外部干扰影响着主车，并进一步影响其能耗。 为了以节能的方式使用模型预测控制（MPC）来控制主车，并且进一步提高生态自适应巡航控制（EACC）策略的性能，预测目标车辆的未来速度是至关重要的。 为此，本研究探讨了基于深度循环神经网络的车辆速度预测方法，其中包括长短期记忆网络（LSTM）和门控循环单元（GRU）。 除此之外，还讨论了基于物理的恒速（CV）和恒加速度（CA）模型。 用于训练的顺序时间序列数据（例如，通过车对车（V2V）通信获得的目标车辆及其前车的速度轨迹，通过车对基础设施（V2I）通信收集的道路限速、交通信号灯当前及未来的相位信息）来自在微观交通模拟器SUMO中创建的城市和高速公路网络。 所提出的速度预测模型针对目标车辆未来速度的长期预测（最多10秒）进行了评估。 此外，结果显示基于LSTM的速度预测器在未见测试数据集上实现了更高的预测精度，从而表现出更好的泛化能力。 此外，还评估了配备EACC的主车在预测速度下的性能，并展示了不同预测范围下的节能效益。