Understanding cyclists' perception of driverless vehicles through eye-tracking and interviews
<|reference_start|>Understanding cyclists' perception of driverless vehicles through eye-tracking and interviews: As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants' gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.<|reference_end|>
@article{berge2024understanding,
    title={Understanding cyclists' perception of driverless vehicles through
  eye-tracking and interviews},
    author={Siri Hegna Berge, Joost de Winter, Dimitra Dodou, Amir Pooyan Afghari,
  Eleonora Papadimitriou, Nagarjun Reddy, Yongqi Dong, Narayana Raju, Haneen
  Farah},
    journal={arXiv preprint arXiv:2408.10064},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10064},
    primaryClass={cs.RO}
}