The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?

TL;DR

This study evaluates human ability to predict lane changes on highways using video data, revealing that humans generally detect lane changes only after they occur, which provides a baseline for AI prediction performance.

Contribution

The paper provides an analysis of human lane change prediction capabilities using the PREVENTION dataset, highlighting limitations and establishing a benchmark for AI systems.

Findings

Most participants detect lane changes after they start.

Humans struggle to anticipate lane changes before they occur.

Results can serve as a baseline for AI prediction evaluation.

Abstract

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.