no code implementations • 26 Jul 2022 • Yue Zhang, Yajie Zou, Yuanchang Xie, Lei Chen
A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles.
no code implementations • 31 Jul 2021 • Yue Zhang, Yajie Zou, Lingtao Wuand Wanbing Han
This study develops a primitive-based framework to identify the driving patterns during merging processes and reveal the evolutionary mechanism at freeway on-ramps in congested traffic flow.
no code implementations • 22 May 2021 • Yue Zhang, Yajie Zou, Lingtao Wu
This study explores the spatiotemporal evolution law and risk formation mechanism of the LC interactive patterns and the findings are useful for comprehensively understanding the latent interactive patterns, improving the rationality and safety of autonomous vehicle's decision-making.
no code implementations • 1 Nov 2020 • Yue Zhang, Yajie Zou, Jinjun Tang, Jian Liang
To capture the stochastic time series of lane-changing behavior, this study proposes a temporal convolutional network (TCN) to predict the long-term lane-changing trajectory and behavior.