Abstract
To further enhance the role of connected vehicles with the Human-Machine Interface (HMI) in helping drivers in foggy weather, it is meaningful to optimize the HMI based on the driver’s needs. In order to explore the method of HMI optimization, this paper builds a driving simulation experimental test platform of freeway connected vehicle system, designing two experimental scenarios according to the technical conditions (without HMI or with HMI). After that, the paper uses Markov chain to explore the drivers’ fixation transition to identify the driver’s needs in different sections at different conditions. Besides, combined with the visual trajectory results, the paper provides suggestions for optimizing HMI. The results show the review rate of drivers in each section for the straight upper front area is very high. The highest value occurs in the heavy fog zone, suggesting that HMI can provide more road information. HMI should offer a prompt to the driver before entering the warning zone, and remind the driver of changes in the speed limit before entering the fog area. The prompt module of HMI should shorten the horizontal length. In conclusion, this paper aims to propose a general diagnosis method by diagnosing the self-designed HMI.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdel-Aty, M., Pande, A., Lee, C., Gayah, V. and Santos, C. D. (2007). Crash risk assessment using intelligent transportation systems data and real-time intervention strategies to improve safety on freeways. J. Intelligent Transportation Systems 11, 3, 107–120.
Administration of Quality Supervision, Inspection and Quarantine, China National Standardization Management Committee (2015). Standard for Expressway Traffic Safety Control under Fog Weather Conditions (GB/T 31445-2015). Standard Press of China, Beijing, China.
Arnaout, G. and Bowling, S. (2011). Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp. J. Industrial Engineering and Management (JIEM) 4, 4, 699–717.
Brooks, J. O., Crisler, M. C., Klein, N., Goodenough, R., Beeco, R. W., Guirl, C., Tyler, P. J., Hilpert, A., Miller, Y., Grygier, J., Burroughs, B., Martin, A., Ray, R., Palmer, C. and Beck, C. (2011). Speed choice and driving performance in simulated foggy conditions. Accident Analysis & Prevention 43, 3, 698–705.
Certification and Accreditation Administration of the People’s Republic of China. Regulations for The Implementation of The Road Traffic Safety Law of The People’s Republic of China (2017). http://www.cnca.gov.cn/bsdt/yywzl/flyzcyj/zcfg/201707/t20170711_54697.shtml.
Chang, X., Li, H., Rong, J., Huang, Z., Chen, X. and Zhang, Y. (2019). Effects of on-board unit on driving behavior in connected vehicle traffic flow. J. Advanced Transportation, 2019, 8591623.
Charlton S. G. (2009). Driving while conversing: Cell phones that distract and passengers who react. Accident Analysis & Prevention 41, 1, 160–173.
Chen, C., Zhao, X., Liu, H., Ren, G., Zhang, Y. and Liu, X. (2019). Assessing the influence of adverse weather on traffic flow characteristics using a driving simulator and VISSIM. Sustainability 11, 3, 830–845.
China Meteorological Administration (2012). Grade of Fog Forecast (in Chinese) (GB/T 27964-2011). Standard Press of China. Beijing, China.
Chow, S. C., Shao, J., Wang, H. and Lokhnygina, Y. (2017). Sample Size Calculations in Clinical Research. Chapman and Hall/CRC, Florida, USA.
Chrysler, S. T., Williams, A. A., Funkhouser, D. S., Holick, A. J. and Brewer, M.A. (2007). Driver Comprehension of Diagrammatic Freeway Guide Signs. Texas Department of Transportation Research and Technology Implementation Office, Texas, USA.
Ding, H., Zhao, X., Rong, J. and Ma, J. (2013). Experimental research on the effectiveness of speed reduction markings based on driving simulation: A case study. Accident Analysis & Prevention, 60, 211–218.
Du, H., Xu, Y., Zhao, X., Li, Z. and Huang, L. (2019). Research on visual characteristics law and complexity evaluation method of diagrammatic guide signs. J. Chongqing Jiaotong University (Natural Science Edition) 38, 07, 1–6+19.
Gáspár, P., Szalay, Z. and Aradi, S. (2014). Highly Automated Vehicle Systems. Report No. 978-963-313-173–2. BME MOGI: Department of Mechatronics, Optics, and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest.
Ghosh, S. and Lee, T. S. (2010). Intelligent Transportation Systems: Smart and Green Infrastructure Design. CRC Press, Boca Raton, FL, USA.
Green, P. (2008). Driver Interface/HMI standards to minimize driver distraction/overload. SAE Paper No. 2008-21-0002.
Hawkins, R. K. (1988). Motorway traffic behaviour in reduced visibility conditions. Vision in Vehicles II. 2nd Int. Conf. Vision in Vehicles, Nottingham, UK
Hu, Y, Zhu, T., Li, J. and Liu, H. (2017). Research on fixation transition characteristics of drivers in extra-long tunnel. J. Safety Science and Technology 13, 8, 42–48.
Lerman, J. (1996). Study design in clinical research: Sample size estimation and power analysis. Canadian J. Anaesthesia 43, 2, 184–191.
Liao, R. (2018). Research on Ecological Driving of Signalized Intersection Teams in Vehicle and Road Collaborative Environment. Ph. D. Dissertation. Beijing Jiaotong University, Beijing, China.
Lin, Y., Pan, X. and Fang, S. (2005). Relationship between information quantum of traffic guide sign and its cognition. J. Transportation Engineering and Information 3, 3, 73–77.
Ministry of Transport of the People’s Republic of China (2014). Technical Standard of Highway Engineering (JTGB01-2014).
Nekoui, M. and Pishro-Nik, H. (2009). The effect of VII market penetration on safety and efficiency of transportation networks. Int. Conf. Communications Workshops. Dresden, Germany.
Odentrantz, J. (2000). Markov chains: Gibbs fields, Monte Carlo simulation, and queues. Technometrics 42, 4, 438–439.
Pan, S., Guo, T., Shao, F. and Xu, Q. (2018). Research on fixation transfer characteristics of drivers driving through urban tunnel. China Safety Science J. 28, 8, 19.
Santos, J., Merat, N., Mouta, S., Brookhuis, K. and De Waard, D. (2005). The interaction between driving and in-vehicle information systems: Comparison of results from laboratory, simulator and real-world studies. Transportation Research Part F: Traffic Psychology and Behaviour 8, 2, 135–146.
Sivak, M. (1996). The information that drivers use: Is it indeed 90% visual?. Perception, 25, 9, 1081–1089.
Underwood, G., Crundall, D. and Chapman, P. (2002). Selective searching while driving: The role of experience in hazard detection and general surveillance. Ergonomics 45, 1, 1–12.
Wang, X. and Wang, X. (2018). Speed change behavior on combined horizontal and vertical curves: Driving simulator-based analysis. Accident Analysis & Prevention, 119, 215–224.
Wang, Z., Chen, Y., Chen, N. and Han, W. (2016). A control strategy of urban expressway under CVIS. Int. J. Simulation Systems, Science & Technology 17, 6, 630–634.
Wen, X. (2019). Summary of distracted driving behavior. Qinghai Transportation Science and Technology 2019, 02, 24–26.
Wen, J. Z., Wang, Z., Du, Z. G. and Wang, S. S. (2019). Fixation distribution and transition of small-radius optically long tunnel in mountain area. Traffic Information and Safety 6, 37, 40–47.
Wu, Y, Abdel-Aty, M., Park, J. and Zhu, J. (2018). Effects of connected-vehicle warning systems on rear-end crash avoidance behavior under fog conditions. Transportation Research Part C: Emerging Technologies, 95, 481–492.
Yakub, F., Lee, S. and Mori, Y. (2016). Comparative study of MPC and LQC with disturbance rejection control for heavy vehicle rollover prevention in an inclement environment. J. Mechanical Science and Technology 30, 8, 3835–3845.
Yonas, A. and Zimmerman, L. (2006). Improving the ability of drivers to avoid collisions with snowplows in fog and snow. ITS Institute, Minnesota Department of Transportation, Report No. MN/RC-2006–29.
Yuan, W., Fu, Y, Guo, Y. S. and Xue, C. L. (2010). Analysis of driver’s visual transition characters with Markov chain. Int. Conf. Power Electronics & Intelligent Transportation System, Qingdao, China.
Zhao, X, Xu, W. and Yao, Y. (2017). A study on a system of dynamic driving safety education based on simulator. Traffic Information and Safety 35, 5, 123–130.
Zhao, X, Xu, W., Ma, J., Li, H., Chen, Y. and Rong, J. (2019a). Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving. Accident Analysis & Prevention, 128, 206–216.
Zhao, X. H., Chen, Y. F., Li, H. J., Xing, G. Y. and Feng, X. F. (2019b). Comprehensive test and impact assessment for human factors of connected vehicle. China J. Highways and Transport 32, 6, 248–261.
Zhao, X. H., Huang, L H. and Rong, J. H. (2015). Influence of the interchange pattern guide sign on running speed in an urban complex interchange. J. Beijing University of Technology 41, 9, 1405–1414.
Acknowledgement
National Natural Science Foundation of China (NO. 52072012), Traffic risk identification, evolution and cause research in a data-driven approach based on aggressive driving behavior.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hu, D., Yang, X., Zhao, X. et al. Method of HMI Optimization Design Based on Fixation Transition Characteristics and Visual Attention Trajectory: A Driver Simulator Study. Int.J Automot. Technol. 23, 1127–1140 (2022). https://doi.org/10.1007/s12239-022-0099-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-022-0099-6