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Found 4 papers, 0 papers with code
Modeling mandatory and discretionary lane changes using dynamic interaction networks
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.
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps
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.
V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes
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.
A Lane-Changing Prediction Method Based on Temporal Convolution Network
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.
