Wang, Hanfeng (Nanyang Technoloical University), Lu, Yun (Nanyang Technological University), Su, Rong (Nanyang Technological University), Luo, Ruikang (School of Electrical and Electronic Engineering, Nanyang Technol), Zhao, Nanbin (Nanyang Technological University), de Boer, Niels (Nanyang Technological University), Guan, Yong Liang (Nanyang Technological University)

Trajectory and Velocity Prediction of Cut-In Vehicles with Deep Learning Method

Scheduled for presentation during the Invited Session "Emerging Data-driven Technologies and Machine Intellection for Smart Traffic Applications" (FrAT6), Friday, September 27, 2024, 10:50−11:10, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Numerous studies have been conducted to predict lane-change trajectories. The significant differences between cut-ins and other lane changes suggest the necessity of building specialized algorithms tailored to learning vehicle cut-ins. In this paper, we explore predicting the trajectory and velocity of the cut-in vehicles with a deep learning method. Particularly, we propose a prediction algorithm by combining a Transformer-based encoder and an LSTM-based decoder. The Transformer-based encoder is applied to capture features related to the driving context of the cut-in vehicle. The LSTM decoder is employed to predict the trajectory and velocity of the cut-in vehicles by considering their temporal and social relationships. We extracted the cut-in events from NGSIM dataset for algorithm evaluation. We compared the performance of the proposed algorithm and three other deep learning algorithms based on the extracted cut-in events. The results suggest that the proposed algorithm outperforms other algorithms in trajectory and velocity predictions of the cut-in vehicles. Moreover, we analyze the effect of the historical data window size on the prediction performance of the proposed algorithm.