Lyu Ying, Wang Yulin, Liu Huan, Dong Xiaoyu, He Yifan, Ren Yilong
Key Laboratory of Advanced Vehicle Integration and Control, Changchun 130011, China.
China FAW Corporation Limited, Changchun 130011, China.
Sensors (Basel). 2025 Aug 28;25(17):5342. doi: 10.3390/s25175342.
With the development of intelligent connected vehicle and artificial intelligence technologies, mixed traffic scenarios where autonomous and human-driven vehicles coexist are becoming increasingly common. Autonomous vehicles need to accurately predict the lane change behavior of preceding vehicles to ensure safety. However, lane change behavior of human-driven vehicles is influenced by both environmental factors and driver preferences, which increases its uncertainty and makes prediction more difficult. To address this challenge, this paper focuses on the mining of driving preferences and the prediction of lane change behavior. We clarify the definition of driving preference and its relationship with driving style and construct a representation of driving operations based on vehicle dynamics parameters and statistical features. A preference feature extractor based on the SimCLR contrastive learning framework is designed to capture high-dimensional driving preference features through unsupervised learning, effectively distinguishing between aggressive, normal, and conservative driving styles. Furthermore, a dual-branch lane change prediction model is proposed, which fuses explicit temporal features of vehicle states with implicit driving preference features, enabling efficient integration of multi-source information. Experimental results on the HighD dataset show that the proposed model significantly outperforms traditional models such as Transformer and LSTM in lane change prediction accuracy, providing technical support for improving the safety and human-likeness of autonomous driving decision-making.
随着智能网联汽车和人工智能技术的发展，自动驾驶车辆和人类驾驶车辆共存的混合交通场景越来越普遍。自动驾驶车辆需要准确预测前车的变道行为以确保安全。然而，人类驾驶车辆的变道行为受到环境因素和驾驶员偏好的双重影响，这增加了其不确定性，使得预测更加困难。为应对这一挑战，本文聚焦于驾驶偏好挖掘和变道行为预测。我们明确了驾驶偏好的定义及其与驾驶风格的关系，并基于车辆动力学参数和统计特征构建了驾驶操作的表示。设计了一种基于SimCLR对比学习框架的偏好特征提取器，通过无监督学习捕获高维驾驶偏好特征，有效区分激进、正常和保守的驾驶风格。此外，提出了一种双分支变道预测模型，该模型将车辆状态的显式时间特征与隐式驾驶偏好特征相融合，实现多源信息的高效整合。在HighD数据集上的实验结果表明，所提出的模型在变道预测准确性方面显著优于Transformer和LSTM等传统模型，为提高自动驾驶决策的安全性和拟人化提供了技术支持。
