Advertisement

The Research of Multilevel Takeover Alert Information Design for Highly Automated Driving Vehicles

  • Lijun Jiang
  • Simin Cao
  • Zhelin LiEmail author
  • Yu Zhang
  • Zequan Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 576)

Abstract

The highly automated driving vehicles relief a certain amount of driving loads but also bring problems on how to get drivers’ attention back to control when it is necessary. This paper proposed a design concept of multilevel alert takeover information and developed a simulated driving system, and the investigated questionnaires were used to evaluate the takeover performance and user experience. Results show that both levels and channels affect alert efficiency. Multilevel alerts are not able to significantly enhance driving takeover performance but effectively improve user experience. Auditory alert information enables drivers to perceive the risks more quickly, while visual alert information assists drivers to learn about the risk degree more efficiently. It is suggested to add personal experience to motivate the takeover ambitious in further study.

Keywords

Automated driving Takeover system Multilevel Alert design 

Notes

Compliance with Ethical Standards

The study was approved by the Logistics Department for Civilian Ethics Committee of School of Design, South China University of technology, and Guangdong Engineering Research Center of Human-Computer Interaction.

All subjects who participated in the experiment were provided with and signed an informed consent form.

All relevant ethical safeguards have been met with regard to subject protection.

References

  1. 1.
    Biswas M, Xu S (2015) 47.3: invited paper: world fixed augmented-reality HUD for smart notifications. SID Sympos Digest of Tech Pap 46(1):708–711CrossRefGoogle Scholar
  2. 2.
    Cano E, González P, Maroto M, Villegas D (2018) Head-up displays (HUD) in drivingGoogle Scholar
  3. 3.
    Curtis D, Mizell D, Gruenbaum P, Janin A (1999) Several devils in the details: making an AR application work in the airplane factory. In: International workshop on Augmented reality: placing artificial objects in real scenes, pp 47–60Google Scholar
  4. 4.
    Gibson M, Lee J, Venkatraman V, Price M, Lewis J, Montgomery O, Mutlu B, Domeyer J, Foley J (2016) Situation awareness, scenarios, and secondary tasks: measuring driver performance and safety margins in highly automated vehicles. SAE Int J Passenger Cars Electron Electr Syst 9(1)CrossRefGoogle Scholar
  5. 5.
    Haeuslschmid R, Schnurr L, Wagner J, Butz A (2015) Contact-analog warnings on windshield displays promote monitoring the road scene. In: Proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications (AutomotiveUI ‘15)Google Scholar
  6. 6.
    Körber M, Radlmayr J, Bengler K (2016) Bayesian highest density intervals of take-over times for highly automated driving in different traffic densities. In: Proceedings of the Human Factors and Ergonomics Society annual meeting, vol 60(1), pp 2009–2013CrossRefGoogle Scholar
  7. 7.
    Liao Y, Sun X, Jia L, Dong H, Zhang Q (2012) Evaluating traffic status of urban expressway in Beijing based on road vehicle capacity. Logistics Technol 31(2):87–89Google Scholar
  8. 8.
    Noy Y, Lemoine T, Klachan C, Burns P (2004) Task interruptability and duration as measures of visual distraction. Appl Ergon 35(3):207–213CrossRefGoogle Scholar
  9. 9.
    Park H, Kim K (2013) Efficient information representation method for driver-centered AR-HUD system. In: Design, user experience, and usability. User experience in novel technological environments, pp 393–400CrossRefGoogle Scholar
  10. 10.
    Radlmayr J, Gold C, Lorenz L, Farid M, Bengler K (2014) How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol 58(1), pp 2063–2067CrossRefGoogle Scholar
  11. 11.
    Tönnis M (2008) Towards automotive augmented realityGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Lijun Jiang
    • 1
    • 2
  • Simin Cao
    • 1
  • Zhelin Li
    • 1
    • 2
    Email author
  • Yu Zhang
    • 1
  • Zequan Zhang
    • 1
  1. 1.School of DesignSouth China University of TechnologyGuangzhouChina
  2. 2.Guangdong Engineering Research Center of Human-Computer Interaction DesignGuangzhouChina

Personalised recommendations