Abstract
Autonomous traffic management (ATM) allows autonomous vehicles to cooperate and can thus lead to an increased traffic efficiency, enhanced traffic safety and decreased ecological footprint of traffic. For the adoption of autonomous vehicles (AVs), humans have to accept and trust them, but so far, the research field of ATM has focused mainly on the aspired safety and efficiency gain instead of an increase in trust and user acceptance of the technology. In this paper, we discuss the need for a human-centered approach for ATM.
For demonstration purposes we designed a turning scenario at an urban intersection with just AVs. The traffic scenario was then simulated with the traffic simulation package SUMO. We implemented a simple ATM system that can interact and manage the behavior of the traffic participants. We present the results of the traffic simulation and the proposed ATM and discuss why the human factors perspective should be considered for the design of ATM. We argue how a human-centered ATM could be used to find a good trade-off between safe and efficient traffic and a comfortable and trustworthy user experience.
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Acknowledgment
This work was supported by the DFG-grants RI 1511/3-1 to JWR, LU 1880/3-1 to AT (both “Learning from Humans – Building for Humans”) and FR 2715/4-1 (“Integrated Socio-technical Models for Conflict Resolution and Causal Reasoning”) to MF.
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Trende, A., Krefting, I., Unni, A., Rieger, J., Fränzle, M. (2022). A Case-Study for a Human-Centered Approach to Traffic Management Systems. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1583. Springer, Cham. https://doi.org/10.1007/978-3-031-06394-7_34
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DOI: https://doi.org/10.1007/978-3-031-06394-7_34
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