Abraham, A., Zhang, Y., & Prasad, S. (2021). Real-Time Prediction of Multi-Class Lane-Changing Intentions based on Highway Vehicle Trajectories. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). doi:10.1109/itsc48978.2021.9564738
Abstract:
For fully automated driving in the real-world,
safety, comfort, and reliability are the key essentials during
operation. However, in the automotive industry, it is critical to
identify the lane change intention of vehicles from the adjacent
lane for safe driving. Thus, the intelligent assistance system
should be smart enough to track such lane change desires and
assist the driver to avoid any accidents that is caused by human
errors on the road. Here, in this paper we propose to predict the
driving behaviors of lane-changing vehicles on the highways by
studying their chronology. Machine learning performs well in
retaining the human-vehicle behavioral patterns and therefore,
we involved random forest to do this prediction. We proposed
new feature space for lane change prediction, where a really
small window size of 3-seconds can lead to high-performance.
The extensive experiments are carried out on real-traffic dataset
from Next Generation SIMulation (NGSIM) where we set up
a new state-of-the-art accuracy of 98.6%.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done