论文标题
IDM追随者：一种模型信息的深度学习方法，用于长期遵循汽车跟踪轨迹预测
IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence Car-Following Trajectory Prediction
论文作者
论文摘要
基于模型和基于学习的方法是两种主要类型的方法，用于对以下行为进行建模。基于模型的方法描述了具有显式数学方程式的汽车跟随行为，而基于学习的方法则致力于在输入和输出之间进行映射。两种方法都有优势和弱点。同时，大多数汽车跟随模型都是生成的，仅考虑上次步骤的速度，位置和加速度的输入。为了解决这些问题，本研究提出了一个名为IDM-乘客的新型框架，该框架可以生成一个经过重复的自动编码器的跟随车辆轨迹的顺序，该自动编码器由物理汽车的模型告知，智能驾驶模型（IDM）。我们实现了一个新颖的结构，具有两个独立的编码器和一个可以依次预测下面的自发性的自我发音器。考虑到与基于模型的预测差异集成的预测和标记数据之间存在差异的损失函数，以更新神经网络参数。具有多个模拟和NGSIM数据集的多个设置的数值实验表明，与仅基于模型或基于学习的方法相比，IDM访问者可以改善预测性能。对不同噪声水平的分析还显示了模型的良好鲁棒性。
Model-based and learning-based methods are two major types of methodologies to model car following behaviors. Model-based methods describe the car-following behaviors with explicit mathematical equations, while learning-based methods focus on getting a mapping between inputs and outputs. Both types of methods have advantages and weaknesses. Meanwhile, most car-following models are generative and only consider the inputs of the speed, position, and acceleration of the last time step. To address these issues, this study proposes a novel framework called IDM-Follower that can generate a sequence of following vehicle trajectory by a recurrent autoencoder informed by a physical car-following model, the Intelligent Driving Model (IDM).We implement a novel structure with two independent encoders and a self-attention decoder that could sequentially predict the following trajectories. A loss function considering the discrepancies between predictions and labeled data integrated with discrepancies from model-based predictions is implemented to update the neural network parameters. Numerical experiments with multiple settings on simulation and NGSIM datasets show that the IDM-Follower can improve the prediction performance compared to the model-based or learning-based methods alone. Analysis on different noise levels also shows good robustness of the model.
