Ensuring Safety in Augmented Reality from Trade-off Between Immersion and Situation Awareness

被引:22
作者
Jung, Jinki [1 ]
Lee, Hyeopwoo [2 ]
Choi, Jeehye [2 ]
Nanda, Abhilasha [2 ]
Gruenefeld, Uwe [3 ,4 ]
Stratmann, Tim Claudius [3 ,4 ]
Heuten, Wilko [4 ]
机构
[1] Korea Res Inst Ships & Ocean Engn, Daejeon, South Korea
[2] Korea Adv Inst Sci & Technol, Daejeon, South Korea
[3] Carl von Ossietzky Univ Oldenburg, Oldenburg, Germany
[4] OFFIS Inst IT, Oldenburg, Germany
来源
PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY (ISMAR) | 2018年
关键词
Human-centered computing; Human computer interaction (HCI); Interaction paradigms; Mixed/augmented reality; Visualization; Visualization design and evaluation methods; DRIVERS;
D O I
10.1109/ISMAR.2018.00032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Although the mobility and emerging technology of augmented reality (AR) have brought significant entertainment and convenience in everyday life, the use of AR is becoming a social problem as the accidents caused by a shortage of situation awareness due to an immersion of AR are increasing. In this paper, we address the trade-off between immersion and situation awareness as the fundamental factor of the AR-related accidents. As a solution against the trade-off, we propose a third-party component that prevents pedestrian-vehicle accidents in a traffic environment based on vehicle position estimation (VPE) and vehicle position visualization (VPV). From a RGB image sequence, VPE efficiently estimates the relative 3D position between a user and a car using generated convolutional neural network (CNN) model with a region-of-interest based scheme. VPV shows the estimated car position as a dot using an out-of-view object visualization method to alert the user from possible collisions. The VPE experiment with 16 combinations of parameters showed that the InceptionV3 model, fine-tuned on activated images yields the best performance with a root mean squared error of 0.34 m in 2.1 ms. The user study of VPV showed the inversely proportional rela- tionship between the immersion controlled by the difficulty of the AR game and the frequency of situation awareness in both quantitatively and qualitatively. Additional VPV experiment assessing two out-of-view object visualization methods (EyeSee360 and Radar) showed no significant effect on the participants' activity, while EyeSee360 yielded faster responses and Radar engendered participants' preference on average. Our field study demonstrated an integration of VPE and VPV which has potentials for safety-ensured immersion when the proposed component is used for AR in daily uses. We expect that when the proposed component is developed enough to be used in real world, it will contribute to the safety-ensured AR, as well as to the population of AR.
引用
收藏
页码:70 / 79
页数:10
相关论文
共 47 条
[1]  
Ali A.A., 2016, P AL SAD INT C MULT, P1, DOI DOI 10.1109/AIC-MITCSA.2016.7759904
[2]  
[Anonymous], 2017, ICML WORKSH MACH LEA
[3]  
[Anonymous], OXFORD MED CASE REPO
[4]  
[Anonymous], ROAD TRAFFIC SIMULAT
[5]  
[Anonymous], 2015, P 13 ANN INT C MOBIL, DOI [10.1145/2742647.2742669, DOI 10.1145/2742647.2742669]
[6]  
[Anonymous], MIL COMM C 2009 MILC
[7]  
[Anonymous], DIG AV SYST C DASC 2
[8]  
[Anonymous], 2017, ARXIV171110453
[9]  
[Anonymous], 2017, ARXIV170805869
[10]  
[Anonymous], 2018, CORR