Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model

Car-following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car-following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car-following models, we proposed a new trajectory generation approach to generate platoon level trajectories given the first leading vehicle's trajectory. In this article, we discussed the temporal and spatial error propagation issue for the traditional approach by a car following block diagram representation. Based on the analysis, we pointed out that error comes from the training method and the model structure. In order to fix that, we adopt two improvements on the basis of the traditional LSTM-based car-following model. We utilized a scheduled sampling technique during the training process to solve the error propagation in the temporal dimension. Furthermore, we developed a unidirectional interconnected LSTM model structure to extract trajectories features from the perspective of the platoon. As indicated by the systematic empirical experiments, the proposed novel structure could efficiently reduce the temporal-spatial error propagation. Compared with the traditional LSTM-based car-following model, the proposed model has almost 40% less error. The findings will benefit the design and analysis of micro-simulation for platoon-level car-following models.

Automated summary

Car-following models have been widely applied and achieved remarkable success in traffic engineering. However, the accuracy of traffic micro-simulation at the platoon level, especially during traffic oscillations, needs improvement. This study proposes a new trajectory generation approach that generates platoon-level trajectories based on the first leading vehicle's trajectory. Through analysis, it is found that the error comes from the training method and the model structure. Two improvements are made to the traditional LSTM-based car-following model, which significantly reduces error in temporal-spatial propagation. Compared to the traditional model, the proposed model has a 40% lower error rate.