TASCC: Driver-Cognition-Oriented Optimal Control Authority Shifting for Adaptive Automated Driving (CogShift)

TASCC：面向驾驶员认知的自适应自动驾驶最优控制权转移（CogShift）

• 批准号: EP/N012089/1
• 负责人: Dongpu Cao
• 金额: $ 202.24万
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN012089%2F1
• 关键词: TASCC; Driver; Cognition; Oriented; Optimal

The emerging development of automated driving demands a mutual understanding and a smooth coordination between human driver and vehicle controller, so as to avoid conflict and mismatch in demands, and instead achieve desirable driving performance, smooth and swift transitions which enhance driving safety during complex operating scenarios. However, such driver-vehicle collaboration during automated driving will impact on the driver's attention and cognition and it is important to consider these effects in order to prevent any negative impact on driving. This project aims to achieve a safe engagement and smooth and swift control-authority shift between the driver and the vehicle controller during adaptive automated driving. To this aim, we will first conduct a comprehensive study of driver attention and cognitive control characteristics when interacting with the vehicle controller. An optimal control authority shifting system which considers driver cognition will then be systematically developed and validated. This cross-disciplinary research challenge will be addressed using a unique combination of researchers from engineering, cognitive neuroscience and human factors. The research will not only contribute to the cutting-edge technology innovations in automated driving, but will also result in a major advance in the science of human attention and cognitive control when interacting with automation.

自动驾驶的发展要求人类驾驶员和车辆控制器之间相互理解和协调，以避免需求冲突和不匹配，而是实现理想的驾驶性能，平滑和快速的过渡，从而提高复杂操作场景下的驾驶安全性。然而，在自动驾驶过程中，这种驾驶员与车辆的协作会影响驾驶员的注意力和认知，因此必须考虑这些影响，以防止对驾驶产生任何负面影响。该项目旨在实现自适应自动驾驶过程中驾驶员和车辆控制器之间的安全接合和平稳快速的控制权限转换。为此，我们将首先进行全面的研究，驾驶员的注意力和认知控制特性时，与车辆控制器进行交互。一个最佳的控制权转移系统，考虑驾驶员的认知，然后将系统地开发和验证。这个跨学科的研究挑战将通过工程，认知神经科学和人为因素的独特组合来解决。该研究不仅将有助于自动驾驶领域的尖端技术创新，还将在人类注意力和认知控制科学与自动化交互时取得重大进展。

项目成果

Characterisation of driver neuromuscular dynamics for haptic take-over system design for automated vehicles

• DOI: 10.1109/iecon.2017.8216786
• 发表时间: 2017-10
• 期刊: IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
• 作者: Chen Lv;Huaji Wang;Dongpu Cao;Yifan Zhao;D. Auger;M. Sullman;R. Matthias;L. Skrypchuk;A. Mouzakitis

A Dual-Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring

• DOI: 10.1109/tits.2019.2939676
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Lichao Yang;Kuo Dong;A. Dmitruk;J. Brighton;Yifan Zhao

Perceptual Load and Inattentional Deafness During Natural Scene Perception

自然场景感知过程中的知觉负荷和无意耳聋

• 发表时间: 2019
• 期刊: PERCEPTION
• 影响因子: 1.7
• 作者: Chech Luca

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

• DOI: 10.1016/j.measurement.2017.10.002
• 发表时间: 2018
• 期刊: Measurement
• 影响因子: 5.6
• 作者: Yang Xing;Chen Lv;Dongpu Cao;Huaji Wang;Yifan Zhao

The effect of perceptual load on gaze and EEG signals in multi-target visual search with free eye-movements

自由眼动多目标视觉搜索中感知负荷对凝视和脑电图信号的影响

• DOI: 10.1167/19.10.273
• 发表时间: 2019
• 期刊: Journal of Vision
• 影响因子: 1.8
• 作者: Harris A