Zhao, Nanbin (Nanyang Technological University), Lu, Yun (Nanyang Technological University), Wang, Bohui (Xi'an Jiaotong University), Cheng, Xinyi (Nanyang Technological University), Su, Rong (Nanyang Technological University), Luo, Ruikang (School of Electrical and Electronic Engineering, Nanyang Technol), Song, Yaofeng (Nanyang Technological University), Zhou, Yao (Nanyang Technopreneurship Center, Nanyang Technological Universi)

Transferring Knowledge from Observed to Unknown: A Data-Driven Lane Change Trajectory Prediction Strategy Based on the Concept of Few-Shot Learning

Scheduled for presentation during the Invited Session "Emerging Data-driven Technologies and Machine Intellection for Smart Traffic Applications" (FrAT6), Friday, September 27, 2024, 10:30−10:50, 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 October 8, 2024

Abstract

Existing data-driven lane change trajectory prediction methods lack the capability to achieve broad consistency in the complex and variable real-world traffic scenarios due to their reliance on training with pre-collected static datasets from fixed scenarios. In reality, the scenarios vary widely, and there is no assurance of sufficient training data for each scenario. The primary challenge is to effectively apply knowledge from observed scenarios to unknown ones while ensuring the method adapts quickly to limited data without compromising prediction accuracy. Based on the aforementioned research gap, this paper develops a novel data-driven lane change prediction method which enhances the transfer of knowledge from observed to unknown scenarios using the few-shot learning (FSL) concept. Our proposed method enables a pre-trained LSTM model to be quickly deployed in new scenarios with only a few samples. Extensive experiments have been conducted using the NGSIM dataset to demonstrate the performance of our method.