Department of Information Display, Kyung Hee University, Seoul, South Korea.
Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
PLoS One. 2023 Sep 1;18(9):e0291043. doi: 10.1371/journal.pone.0291043. eCollection 2023.
Investigating the factors underlying perceived speed and risk is crucial to ensure safe driving. However, existing studies on this topic usually measure speed and risk perception indirectly after a driving session, which makes it difficult to trace dynamic effects and time points of potential misestimates. To address this problem, we developed and validated a novel continuous method for dynamically measuring risk and speed perceptions. To study the factors affecting risk and speed perception, we presented participants with videos captured on the same racing track from the same point of view but with different drivers who varied in their speed and risk profiles. During the experiment, participants used a joystick to continuously rate the subjectively perceived risk of driving in the first block and the perceived speed in the second block. Our analysis of these dynamic ratings indicates that risk and speed estimates were decoupled, with curves resulting in decreased speeds but increased risk ratings. However, a close distance to the car in front increased both speed and risk. Based on actual and estimated speed data, we found that overtaking cars on curves resulted in participants overestimating their own speed, whereas an increase in the distance to the car in front on a straight course led to underestimations of their own speed. Our results showcase the usefulness of dynamic rating profiles for in-depth investigations into situations that could result in drivers misjudging speed or risk and will thus help the development of more intelligent, human-centered driving assistance systems.
研究感知速度和风险的背后因素对于确保安全驾驶至关重要。然而，现有关于这个主题的研究通常在驾驶过程后间接测量速度和风险感知，这使得难以追踪潜在误估计的动态效应和时间点。为了解决这个问题，我们开发并验证了一种用于动态测量风险和速度感知的新方法。为了研究影响风险和速度感知的因素，我们向参与者展示了从相同视角拍摄的同一赛车场的视频，但驾驶员的速度和风险特征不同。在实验中，参与者使用操纵杆在第一块中连续评估主观感知的驾驶风险，在第二块中评估感知速度。我们对这些动态评级的分析表明，风险和速度估计是解耦的，曲线导致速度降低但风险评级增加。然而，与前车的近距离增加了速度和风险。根据实际和估计的速度数据，我们发现弯道上超车会导致参与者高估自己的速度，而直道上与前车的距离增加会导致对自己速度的低估。我们的研究结果展示了动态评分曲线在深入研究可能导致驾驶员误判速度或风险的情况方面的有用性，从而有助于开发更智能、以人为中心的驾驶辅助系统。
