Predicting Vehicles' Longitudinal Trajectories and Lane Changes on Highway On-Ramps
This work addresses traffic congestion and safety issues for highway on-ramps, but it is incremental as it builds on existing prediction methods.
The paper tackles predicting vehicle trajectories and lane changes on highway on-ramps to reduce congestion, achieving improved accuracy over a traditional LSTM model using NGSIM data.
Vehicles on highway on-ramps are one of the leading contributors to congestion. In this paper, we propose a prediction framework that predicts the longitudinal trajectories and lane changes (LCs) of vehicles on highway on-ramps and tapers. Specifically, our framework adopts a combination of prediction models that inputs a 4 seconds duration of a trajectory to output a forecast of the longitudinal trajectories and LCs up to 15 seconds ahead. Training and Validation based on next generation simulation (NGSIM) data show that the prediction power of the developed model and its accuracy outperforms a traditional long-short term memory (LSTM) model. Ultimately, the work presented here can alleviate the congestion experienced on on-ramps, improve safety, and guide effective traffic control strategies.