Welcome to Journal of Graphics share: 

Journal of Graphics ›› 2025, Vol. 46 ›› Issue (4): 909-918.DOI: 10.11996/JG.j.2095-302X.2025040909

• Industrial Design • Previous Articles     Next Articles

Design model of in-vehicle auxiliary information based on risk representation

KE Shanjun(), WANG Yumiao, NIE Chengyang, HE Bangsheng, GUO Dong()   

  1. School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400050, China
  • Received:2024-12-02 Revised:2025-03-10 Online:2025-08-30 Published:2025-08-11
  • Contact: GUO Dong
  • About author:First author contact:

    KE Shanjun (1975-), associate professor, master. His main research interests cover user experience and human-computer interaction. E-mail:shanjunke@cqut.edu.cn

  • Supported by:
    Chongqing Natural Science Foundation of China(CSTB2023NSCQ-LZX0161)

Abstract:

In order to study how information can accurately characterize risk to assist drivers in accurately perceiving the environment, information samples containing different modalities, variables and parameters were designed to conduct urgency and annoyance perception measurement experiments. Based on the results of the perception measurements, a three-level in-vehicle auxiliary information design model containing modal ordering, variable selection and parameter optimization was constructed. Firstly, for visual, auditory and tactile modalities, the differential sensitivity of urgency perception of each design variable was compared. The design variable with the highest differential sensitivity was selected as the risk characterization variable of the modality, and the risk changes were characterized by the parameter level changes of the modality. Second, for each non-risk characterization variable, differences between the perceived urgency and disturbance at different parameter levels were compared, the parameter level with the largest difference was designated the optimal parameter level for the variable, and the auxiliary information model was constructed for each modality by combining the risk characterization variables. Then, linear equations of urgency and perturbation were fitted for the three modes, the difference in perturbation of each modality under the same urgency was observed, and the modes were prioritized according to the principle of high urgency and low perturbation. Finally, each modal auxiliary information model was superimposed in order to form a multimodal in-vehicle auxiliary information model with “4-level visual flicker frequency + 5-level tactile vibration duty cycle + 6-level auditory pulse gap”. The constructed in-vehicle auxiliary information model can accurately characterize 15 levels of environmental risks, thereby supporting accurate driver perception and enhancing driving safety.

Key words: in-vehicle auxiliary information, risk characterization, perceived urgency, intrusiveness, differential sensitivity

CLC Number: