Delft University of Technology, Netherlands.
University of Leeds, UK.
Hum Factors. 2024 May;66(5):1399-1413. doi: 10.1177/00187208221144561. Epub 2022 Dec 19.
We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers.
Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving.
The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions.
Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks.
Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.
通过对人类驾驶员左转弯间隙接受行为背后的认知过程进行建模，弥合自然驾驶行为研究与现代认知神经科学决策理论之间的差距。
理解人类驾驶员的决策对于开发安全、高效的交通系统至关重要。当前驾驶员决策模型对潜在的认知过程几乎没有深入了解。另一方面，实验室中对抽象、高度受控任务的研究表明，噪声证据积累是决策的关键机制。然而，目前还不清楚在这些任务中涉及的认知过程对于那些在更复杂行为中根深蒂固的决策是否同样重要，例如驾驶。
驾驶员接受可用间隙的概率随间隙大小而增加；重要的是，反应时间随时间间隙而增加，但不随距离间隙而增加。广义漂移扩散模型解释了观察到的决策结果和反应时间分布，以及这些结果中的大量个体差异。通过交叉验证，我们证明该模型不仅解释了数据，还可以推广到样本外条件。
我们的研究结果表明，动态证据积累是人类驾驶员左转弯间隙接受决策的关键机制，并说明了简单的认知过程模型如何帮助理解复杂现实任务中的人类行为。
我们研究结果的潜在应用包括通过自动驾驶车辆实时预测人类行为，以及在自动驾驶的虚拟环境中模拟逼真的人类行为。
