Abstract
With the development of science and technology, many new human-machine interaction methods have appeared in cars. Therefore, how to improve the interaction efficiency in human-machine interaction has become one of the important research topics. Our research focuses on the interaction between car cockpit and driver. To make a usability evaluation of car cockpit, we designed multiple sets of comparative experiments with different concurrent tasks. In the experiment, we collected front scene binocular image and car speed to calculate driving performance, driver’s heart rate and eye movement to represent driver’s physiological state. Specifically, for front scene analysis, we simplified the feature point matching method and obtained quite accurate object distance estimation. Experimental data showed that car speed was closer to the required speed in speed control task than speed + direction control task or speed + temperature control task; distance was adjusted better in distance control task than distance + temperature control task; driver’s heart rate was higher and has more fluctuation during the operation of secondary tasks; driver diverted their visual attention from the road to inside instruments more frequently during manual control than voice control. These results indicate when the task is more difficult or there is interference from secondary task, the driving performance would decrease and driver would be more stressed. And manual control task is more disruptive to driving performance than voice control task, but it takes more time. Finally, driving will be safer and more effective when using voice control instead of manual control.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel, B.N., Rosenberg, L., Willcox, G., et al.: Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit. Med. 2(1), 1–10 (2019)
Zhang, S., Lu, Y., Fu, S.: Recognition of the cognitive state in the visual search task. In: Ayaz, H. (ed.) AHFE 2019. AISC, vol. 953, pp. 363–372. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20473-0_35
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 7–42 (2002). https://doi.org/10.1023/A:1014573219977
Zhang, K., Fang, Y., Min, D., et al.: Cross-scale cost aggregation for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1590–1597. IEEE (2014)
Liu, C., Yuen, J., Torralba, A.: SIFT flow: dense correspondence across scenes and its applications. TPAMI 33(5), 978–994 (2011)
Mei, X., Sun, X., Dong, W., Wang, H., Zhang, X.: Segment-tree based cost aggregation for stereo matching. In: CVPR, pp. 313–320. IEEE (2013)
Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: CVPR, pp. 504–511. IEEE (2011)
Wang, Z.-F., Zheng, Z.-G.: A region based stereo matching algorithm using cooperative optimization. In: CVPR, pp. 1–8. IEEE (2008)
Yang, Q., Wang, L., Yang, R., Stewénius, H., Nistér, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. TPAMI 31(3), 492–504 (2008)
Zhang, Z.: A flexible new technique for camera calibration. TPAMI 22(11), 1330–1334 (2000)
Ma, L., Li, J., Ma, J., et al.: A modified census transform based on the neighborhood information for stereo matching algorithm. In: 2013 Seventh International Conference on Image and Graphics. pp. 533–538. IEEE (2013)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Ok, S.-H., Shim, J.H., Moon, B.: Modified adaptive support weight and disparity search range estimation schemes for stereo matching processors. J. Supercomput. 74(12), 6665–6690 (2017). https://doi.org/10.1007/s11227-017-2058-y
Choi, N., Jang, J., Paik, J.: Illuminant-invariant stereo matching using cost volume and confidence-based disparity refinement. JOSA A 36(10), 1768–1776 (2019)
Kumar, S., Micheloni, C., Piciarelli, C., et al.: Stereo rectification of uncalibrated and heterogeneous images. Pattern Recogn. Lett. 31(11), 1445–1452 (2010)
Tran, T.N., Drab, K., Daszykowski, M.: Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometr. Intell. Lab. Syst. 120, 92–96 (2013)
Arunkumar, N., et al.: K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput. 23(19), 9083–9096 (2018). https://doi.org/10.1007/s00500-018-3618-7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, C., Wang, Z., Fu, S. (2020). Usability Evaluation of Car Cockpit Based on Multiple Objective Measures. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Cognition and Design. HCII 2020. Lecture Notes in Computer Science(), vol 12187. Springer, Cham. https://doi.org/10.1007/978-3-030-49183-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-49183-3_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49182-6
Online ISBN: 978-3-030-49183-3
eBook Packages: Computer ScienceComputer Science (R0)