The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
A. Quintanar, R. Izquierdo, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?," 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, 2020, pp. 45-50, doi: 10.1109/IV47402.2020.9304640.
While driving on highways, every driver tries to
be aware of the behavior of surrounding vehicles, including
possible emergency braking, evasive maneuvers trying to avoid
obstacles, unexpected lane changes, or other emergencies that
could lead to an accident. In this paper, human’s ability to
predict lane changes in highway scenarios is analyzed through
the use of video sequences extracted from the PREVENTION
dataset, a database focused on the development of research
on vehicle intention and trajectory prediction. Thus, users
had to indicate the moment at which they considered that a
lane change maneuver was taking place in a target vehicle,
subsequently indicating its direction: left or right. The results
retrieved have been carefully analyzed and compared to ground
truth labels, evaluating statistical models to understand whether
humans can actually predict. The study has revealed that most
participants are unable to anticipate lane-change maneuvers,
detecting them after they have started. These results might serve
as a baseline for AI’s prediction ability evaluation, grading if
those systems can outperform human skills by analyzing hidden
cues that seem unnoticed, improving the detection time, and
even anticipating maneuvers in some cases.