Keqi Chen
ABSTRACT
Today's digitalization has become a staple of life, and consumers' escalating digital lifestyles and demand for convenient services are directly driving the digital transformation of the automotive industry. The car will no longer be a single-function transport but will become an intelligent mobile terminal providing mobility services. The influx of digital services into the car will result in a dramatic transformation of the relationship between the driver and the car, where the car will act as an intelligent mobile terminal to transfer and communicate information with people or the surrounding environment, which will lead to an explosion of information in the car, and due to the limitations of human information processing capabilities, this may make the currently established design of human-computer interaction in the car no longer applicable. If we continue to view the human-vehicle relationship through the lens of a control-oriented machine interface, this will place strong constraints on the design of intelligent human-vehicle interaction. The core of the future intelligent vehicle HCI field is a richer and more diverse information interaction between vehicle, human, and environment, so changing the concept from HCI to human-vehicle information interaction may well support the development of future intelligent vehicle systems. In this article, the exploitation of a human-vehicle interaction assistance system for the handover of control of a vehicle under conditional automation is used as an example to discuss how human information interaction is applicable to the automotive domain.
- Guo, J., Song, B., He, Y., Yu, F. R., & Sookhak, M. (2017). A survey on compressed sensing in vehicular infotainment systems. IEEE Communications Surveys & Tutorials, 19(4), 2662-2680.Google Scholar
Cross Ref
- Kazmi, S. A., Dang, T. N., Yaqoob, I., Ndikumana, A., Ahmed, E., Hussain, R., & Hong, C. S. (2019). Infotainment enabled smart cars: A joint communication, caching, and computation approach. IEEE Transactions on Vehicular Technology, 68(9), 8408-8420.Google ScholarCross Ref
- Ohn-Bar, E., & Trivedi, M. M. (2014). Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations. IEEE transactions on intelligent transportation systems, 15(6), 2368-2377.Google Scholar
- May, K. R., Gable, T. M., & Walker, B. N. (2014, September). A multimodal air gesture interface for in vehicle menu navigation. In Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 1-6).Google ScholarDigital Library
- Shadrin, S. S., & Ivanova, A. A. (2019). ANALYTICAL REVIEW OF STANDARD SAE J3016 «TAXONOMY AND DEFINITIONS FOR TERMS RELATED TO DRIVING AUTOMATION SYSTEMS FOR ON-ROAD MOTOR VEHICLES» WITH LATEST UPDATES. Avtomobil'. Doroga. Infrastruktura., (3 (21)), 10.Google Scholar
- Kaber, D. B., & Endsley, M. R. (2004). The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theoretical Issues in Ergonomics Science, 5(2), 113-153.Google ScholarCross Ref
- Stanton, N. A., & Young, M. S. (2005). Driver behaviour with adaptive cruise control. Ergonomics, 48(10), 1294-1313.Google ScholarCross Ref
- Russell, H. E., Harbott, L. K., Nisky, I., Pan, S., Okamura, A. M., & Gerdes, J. C. (2016). Motor learning affects car-to-driver handover in automated vehicles. trials, 6(6), 6.Google Scholar
- Skrypchuk, L., Langdon, P., Sawyer, B. D., & Clarkson, P. J. (2020). Unconstrained design: Improving multitasking with in-vehicle information systems through enhanced situation awareness. Theoretical issues in ergonomics science, 21(2), 183-219.Google Scholar
- Gold, C., Körber, M., Lechner, D., & Bengler, K. (2016). Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density. Human factors, 58(4), 642-652.Google ScholarCross Ref
- Forster, Y., Naujoks, F., Neukum, A., Huestegge, L. (2017). Driver compliance to take-over requests with different auditory outputs in conditional automation. Accident Analysis & Prevention, 109, 18–28.Google ScholarCross Ref
- Meng, F., Spence, C. (2015). Tactile warning signals for in-vehicle systems. Accident Analysis & Prevention, 75, 333–346Google ScholarCross Ref
- Politis, I., Brewster, S., Pollick, F. (2017). Using multimodal displays to signify critical handovers of control to distracted autonomous CAR drivers. International Journal of Mobile Human Computer Interaction, 9, 1–16Google ScholarDigital Library
- Petermeijer, S., Doubek, F., de Winter, J. (2017). Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants [Conference session]. IEEE International Conference on Systems, Man, and Cybernetics, 1505–1510.Google ScholarDigital Library
- Louw, T., & Merat, N. (2017). Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation. Transportation Research Part C: Emerging Technologies, 76, 35-50.Google ScholarCross Ref
- Fidel, R. (2012). Human Information Interaction: An Ecological Approach to Information Behavior.Google ScholarCross Ref
- Naikar, N. (2013). Work domain analysis: Concepts, guidelines, and cases. CRC press.Google ScholarCross Ref
- Information Digitization and Artificial Intelligence Applied in Smart Car HCI Development
Recommendations
RFDrive: Tagged Human-Vehicle Interaction for All
Human-vehicle interaction is important for safe driving. The driver needs to interact with the in-vehicle steering wheel and infotainment system properly during driving. Specifically, the driving guidelines require the driver to hold the steering wheel ...
A smart car control model for brake comfort based on car following
This paper demonstrates a novel car-following model focused on passenger comfort, for example, a rapid deceleration will make passengers uncomfortable. The brake comfort model of car following was set up according to the relationship between vehicle ...
Autonomous Driving: Investigating the Feasibility of Bimodal Take-Over Requests
Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal auditory and visual handover ...
Comments