Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles

Lu, Yuhuan ORCID: https://orcid.org/0000-0001-5332-3389, Zhang, Zhen ORCID: https://orcid.org/0009-0007-9955-0916, Wang, Wei ORCID: https://orcid.org/0000-0002-1717-5785, Zhu, Yiting ORCID: https://orcid.org/0000-0003-1113-7507, Chen, Tiantian ORCID: https://orcid.org/0000-0002-8954-1430, Al-Otaibi, Yasser D ORCID: https://orcid.org/0000-0002-1464-8401, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327 and Hu, Xiping ORCID: https://orcid.org/0000-0002-4952-699X (2025) Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles. IEEE Transactions on Intelligent Transportation Systems. pp. 1-12. ISSN 1524-9050

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Abstract

Ensuring the smooth operation of road traffic is a momentous target in Intelligent Transportation Systems, which can be expedited by a secure and reliable Internet of Vehicles (IoV). As prominent carriers of the IoV, intelligent vehicles (IVs), that bear the promising potential for alleviating traffic congestion, have become the core road traffic participants. However, the mixed-traffic environment escalates the risk of IVs, as the discretionary lane change behaviors of nearby human-driven vehicles may result in collisions with IVs, compromising the robust performance of the IoV. Recent studies have utilized advanced deep learning techniques to achieve proactive lane change intention prediction, including Recurrent Neural Networks and Transformer. Although attaining reasonable prediction performance, they adopt the data-driven paradigm, which excessively focuses on learning from data while neglecting the domain knowledge. Against this background, we propose to employ the knowledge-driven paradigm and design KLEP, a knowledge-driven lane change prediction framework. KLEP incorporates driving knowledge into lane change modeling, presenting the top-down hierarchical cognitive process of drivers when performing lane change maneuvers. Extensive experiments conducted on two real-world natural driving datasets demonstrate the effectiveness of KLEP. Compared to state-of-the-art lane change prediction baselines, KLEP consistently outperforms them and achieves average improvements of 6.2-7.1% and 53.0-67.2% on intention classification and intention forecast tasks across different datasets, respectively. We also validate that KLEP has strong interpretability that aligns with real-world physical laws in lane change scenarios and is lightweight enough to fulfill online prediction.