Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

Abstract

• Propose interpretable dimensionality reduction techniques in trajectory prediction. • Piecewise Taylor series approximation and piecewise Fourier series approximation. • Reduce device cost and computing energy consumption by simplifying computation. • Verify the techniques’ effectiveness by comparing them with benchmarks. • Prove the techniques’ robustness against data noises. To facilitate low-cost connected automated vehicle (CAV) system development, this study proposes two interpretable dimensionality reduction techniques in vehicle trajectory prediction, i.e., the piecewise Taylor series approximation (PTA) and the piecewise Fourier series approximation (PFA), to lower computation complexity, reduce device investment, and decrease computation energy consumption. Two benchmarks are developed, the long short-term memory (LSTM)-based model without dimensionality reduction and the LSTM-based model with encoder-decoder (a widely used dimensionality reduction technique). Results show that the four predictions have similar accuracy, and the training time (proportional to computation energy consumption) of models with dimensionality reduction techniques is greatly reduced. The reduction is even more significant when PTA/PFA is used. Sensitivity analysis advises PFA/PTA parameter selections to reduce computation complexity without significant loss of prediction accuracy. Further, the robustness of the LSTM PTA/PFA is proven by the investigation of data noises.