Deep Learning Based Vehicle Position Estimation for Human Drive Vehicle at Connected Freeway

Accurate vehicle position data is essential information for active traffic management in connected freeway. Traffic data of connected vehicles can be collected in real time while the one of the human drive vehicle have to be estimated in connected environment. A vehicle position estimation was proposed for human driving vehicle which are not adjacent to communicated vehicles, where the car-following equation was trained by a complex neural network An improved recurrent neural network(RNN) based on gated recurrent unit (GRU) was adopted in the modeling to solve long-term dependencies. Both historical and present movement data the preceding vehicle were considered in the improved RNN model. Performance of the method was evaluated by vehicle-pair data extracted from NGSIM. The results indicated that the proposed method has higher accuracy than the method based on traditional car-following models.