Johannes-Gutenberg-Universität Mainz, Mainz, Germany.
PLoS One. 2023 Aug 2;18(8):e0288206. doi: 10.1371/journal.pone.0288206. eCollection 2023.
When judging the time-to-collision (TTC) of visually presented accelerating vehicles, untrained observers do not adequately account for acceleration (second-order information). Instead, their estimations only rely on vehicle distance and velocity (first-order information). As a result, they systemically overestimate the TTC for accelerating objects, which represents a potential risk for pedestrians in traffic situations because it might trigger unsafe road-crossing behavior. Can training help reduce these estimation errors? In this study, we tested whether training with trial-by-trial feedback about the signed deviation of the estimated from the actual TTC can improve TTC estimation accuracy for accelerating vehicles. Using a prediction-motion paradigm, we measured the estimated TTCs of twenty participants for constant-velocity and accelerated vehicle approaches, from a pedestrian's perspective in a VR traffic simulation. The experiment included three blocks, of which only the second block provided trial-by-trial feedback about the TTC estimation accuracy. Participants adjusted their estimations during and after the feedback, but they failed to differentiate between accelerated and constant-velocity approaches. Thus, the feedback did not help them account for acceleration. The results suggest that a safety training program based on trial-by-trial feedback is not a promising countermeasure against pedestrians' erroneous TTC estimation for accelerating objects.
当判断视觉呈现的加速车辆的碰撞时间 (TTC) 时,未经训练的观察者不能充分考虑加速度(二阶信息)。相反,他们的估计仅依赖于车辆距离和速度(一阶信息)。因此,他们系统地高估了加速物体的 TTC,这对交通中的行人构成了潜在风险,因为它可能引发不安全的过街行为。培训可以帮助减少这些估计错误吗?在这项研究中,我们测试了通过关于估计的 TTC 与实际 TTC 的符号偏差的逐次反馈进行训练是否可以提高对加速车辆的 TTC 估计准确性。使用预测运动范式,我们从 VR 交通模拟中行人的角度测量了二十名参与者对恒定速度和加速车辆接近的估计 TTC。该实验包括三个块,其中只有第二个块提供了关于 TTC 估计准确性的逐次反馈。参与者在反馈期间和之后调整了他们的估计,但他们无法区分加速和恒定速度的方法。因此,反馈并没有帮助他们考虑加速度。结果表明,基于逐次反馈的安全培训计划并不是针对行人对加速物体错误 TTC 估计的一种有前途的对策。