PDF(Pubmed)
Abstract：
Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver\'s latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.
摘要：
人工智能和智能车辆技术的最新进展有望通过先进的驾驶员辅助系统（ADAS）彻底改变出行和交通。某些认知因素，例如冲动性和抑制性控制已被证明与危险驾驶行为和道路冒险有关。然而,现有系统未能充分利用辅助驾驶技术中的这些因素。改变这些认知因素的水平可能会影响ADAS界面的有效性和接受度。我们展示了一种通过驾驶员安全界面个性化驾驶员交互的方法，该界面是根据驾驶员的潜在认知状态从其驾驶行为中推断而触发的。要做到这一点，我们采用数据驱动的方法，并训练一个递归神经网络，从最近的驾驶行为中推断冲动性和抑制性控制。该网络对人类驾驶员进行了训练，以从最近的驾驶行为中推断出冲动性和抑制性控制。使用从高保真车辆运动模拟器实验中收集的数据，我们证明了从驾驶员行为中推断这些因素的能力。然后，我们使用这些推断的因素来立即确定是否使用驾驶员安全界面。使用实际人类数据使用留一交叉验证来评估该方法。我们的评估表明，我们的个性化驾驶员安全界面可以捕获驾驶员的认知特征，可以通过减少驾驶员在黄光区域中奔跑的倾向来更有效地影响驾驶员的行为。
