Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference
Emily S Sumner, Jonathan DeCastro, Jean Costa, Deepak E Gopinath, Everlyne Kimani, Shabnam Hakimi, Allison Morgan, Andrew Best, Hieu Nguyen, Daniel J Brooks, Ba… 展开作者
Emily S Sumner, Jonathan DeCastro, Jean Costa, Deepak E Gopinath, Everlyne Kimani, Shabnam Hakimi, Allison Morgan, Andrew Best, Hieu Nguyen, Daniel J Brooks, Bassam ul Haq, Andrew Patrikalakis, Hiroshi Yasuda, Kate Sieck, Avinash Balachandran, Tiffany Chen, Guy Rosman
Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Although it is widely recognized that certain cognitive factors, such as impulsivity and inhibitory control, are related to risky driving behavior, play a significant role in on-road risk-taking, existing systems fail to leverage such factors. Varying levels of these cognitive factors could influence the effectiveness and acceptance of driver safety interfaces.
We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are triggered based on a learned recurrent neural network. The network is trained from a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using a high-fidelity vehicle motion simulator, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to make instantaneous determinations on whether or not to engage a driver safety interface. This interface aims to decrease a driver's speed during yellow lights and reduce their inclination to run through them.
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KITE Research Institute（KITE研究机构）；Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）；University of Toronto（多伦多大学）；Department of Biomedical Engineering（生物医学工程系）；Department of Occupational Science and Occupational Therapy（职业科学与职业治疗系）；Department of Psychology（心理学系）
Effects of Cognitive Distraction and Driving Environment Complexity on Adaptive Cruise Control Use and Its Impact on Driving Performance: A Simulator Study
BlurDriving: Investigating How Personalized Blur Techniques Impact Drivers' Performance in Virtual Reality
The University of Tokyo（东京大学）；UCL Interaction Centre（伦敦大学学院交互中心）；Team ARAI, Université Paris-Saclay, CNRS, LISN（巴黎萨克雷大学阿拉伊团队，法国国家科学研究中心，利斯恩实验室）；Institute of Media Informatics, Ulm University（乌尔姆大学媒体信息学研究所）
Institute for Creative Technologies, University of Southern California（南加州大学创意技术研究所）；Marshall School of Business, University of Southern California（南加州大学马歇尔商学院）；Raytheon Technologies（雷神技术公司）；Khoury College of Computer Sciences, Northeastern University（东北大学科利计算机学院）
Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection
German Aerospace Center (DLR)（德国航空航天中心）；Institute for Systems Engineering for Future Mobility（未来出行系统工程研究所）
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
Institute for Intelligent Systems Research and Innovation（智能系统研究与创新研究所）；Faculty of Computing and Information Technology (FoCIT)（计算与信息科技学院）；Swinburne University of Technology（斯威丁大学）
Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Department of Computer Science, Imam Mohammad Ibn Saud Islamic University（伊玛姆·莫哈默德·伊本·沙特伊斯兰大学计算机科学系）；Department of Computer Science, Princess Nourah bint Abdulrahman University（努拉·本·阿卜杜勒拉赫曼大学计算机科学系）
