Factors Influencing and Contributing to Perceived Safety of Passengers during Driverless Shuttle Rides

Abstract

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment.

Perceptions and attitudes of bicyclists towards self-driving cars: a mixed methods approach

“…This relates to other findings from survey studies, like the results from Pyrialakou et al (2020), also observing a gender difference, and Pammer et al (2021) showing that younger bicyclists would trust an AV more. As trust and perceived safety are decisive factors in the adoption and acceptance of AVs (Kyriakidis et al, 2015;Hulse et al, 2018;Molnar et al, 2018;Panagiotopoulos and Dimitrakopoulos, 2018;Stegmüller et al, 2019;Howard and Dai, 2020;Luger-Bazinger et al, 2021), the results further strengthen the need to include bicyclists and their requirements in the development of such emerging technologies.…”

“…The perception of and attitude towards AVs is influenced by a number of variables, like sociodemographic characteristics such as age (Payre et al, 2014;Haboucha et al, 2017), gender (Hohenberger et al, 2016;Rice and Winter, 2019), income and educational level (Fraedrich et al, 2015;Haboucha et al, 2017). The most important factors for acceptance are perceived safety and trust regarding AVs (Kyriakidis et al, 2015;Hulse et al, 2018;Molnar et al, 2018;Panagiotopoulos and Dimitrakopoulos, 2018;Stegmüller et al, 2019;Howard and Dai, 2020;Luger-Bazinger et al, 2021). Indeed, safety of AVs is one of the biggest scepticisms within the public (Haeuslschmid et al, 2017;Hewitt et al, 2019;Bakalos et al, 2020).…”

“…In the second part, we assessed attitudes towards self-driving cars and for this, the concept of self-driving car (SAE level 4 or 5) was introduced and explained and participants were reminded to think about the self-driving car from their perspective as bicyclists. For measuring attitude, we developed two items specifically based on factors that were found relevant for further acceptance of AVs, namely, perceived safety and trust (e.g., Howard and Dai, 2020;Luger-Bazinger et al, 2021). Participants of the survey were also asked about whether they would like to know if they encounter an AV in traffic by knowing about the status (autonomous vs. nonautonomous) and whether they would like to be informed about the next manoeuvre of the AV, as previous studies have indicated that these are important elements especially for eHMI design (Hagenzieker et al, 2019;Kaß et al, 2020).…”

Mining Autonomous Vehicle Driving Boundary Detection on Basis of 3D LiDAR

“…The final output identifies the point cloud segmentation results for different regions. This point cloud segmentation method based on region growth can be applied to fields such as unmanned mining, providing accurate road information and environmental perception [17][18].…”