THESIS
2023
1 online resource (ix, 19 pages) : illustrations (some color)
Abstract
In the task of autonomous driving, safe vehicle motion prediction is a challenging task
not only due to the complicated street environment but also due to uncertain factors
such as unseen vehicles that are outside the scope of the ego vehicle’s perception range.
Henceforth, being able to predict potential trajectories of undetected vehicles is a necessary
part of safe motion prediction. Traditional models rely on historical observation to
predict the motion of surrounding vehicles, but what if there is no historical input? In
this paper, we propose a method that can achieve motion prediction for both seen vehicles
and unseen vehicles, taking both historical trajectories and statistical information as
input and utilizing a self-devised deep-learning model. The prediction for unseen vehic...[
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In the task of autonomous driving, safe vehicle motion prediction is a challenging task
not only due to the complicated street environment but also due to uncertain factors
such as unseen vehicles that are outside the scope of the ego vehicle’s perception range.
Henceforth, being able to predict potential trajectories of undetected vehicles is a necessary
part of safe motion prediction. Traditional models rely on historical observation to
predict the motion of surrounding vehicles, but what if there is no historical input? In
this paper, we propose a method that can achieve motion prediction for both seen vehicles
and unseen vehicles, taking both historical trajectories and statistical information as
input and utilizing a self-devised deep-learning model. The prediction for unseen vehicle
trajectories is represented in heatmaps. Experimental results show that our model is able
to predict not only unseen vehicle trajectories that are in the dataset but also other potential
ones that may appear in the real-world environment. To our best knowledge, safe
motion prediction is a task that hasn’t been well explored, and we hope our work could
inspire more to realize its importance.
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