In urban areas, drivers frequently interact with vulnerable road users. On-road studies have shown that drivers are more likely to have safety-relevant interactions with pedestrians when they are inattentive and when pedestrians behave unexpectedly. Notwithstanding these behavioural effects, most microscopic traffic flow models do not accurately describe driver response to pedestrian crossing behaviour. This study investigates the factors influencing driver behaviour characteristics when pedestrians cross the road in front of the vehicle. The data were collected in the UDRIVE naturalistic driving study in France and the UK. The interactions with pedestrians in daylight were identified using the MobilEye® smart camera. The minimum time to zebra and the maximum deceleration during each interaction were investigated in regression models. The results showed that, controlling for the initial speed of the subject vehicle, the minimum time to zebra during interactions was significantly shorter when the pedestrian crossed while the driver had a green traffic light, the vehicle segment was medium, and other pedestrians had already crossed. Controlling for initial speed and acceleration, the maximum deceleration during interactions was lower when the pedestrian crossed while the driver had a green traffic light, no other pedestrians had already crossed, the pedestrian was not a child, teenager or elderly person, and the pedestrian did not glance toward the vehicle. These factors can be incorporated into traffic simulations to describe driver responses more realistically. Further research is needed to understand the influence of the driver’s state because most drivers looked toward pedestrians.

Background

Automated driving systems (ADS) are exponentially increasing in occurrence and autonomy. Although general rules-of-thumb are slowly being adhered to regarding its human occupant—through Human-Machine Interfaces, take-over requests, etc.—different people respond differently to similar things. Currently, individualising ADS is trending, but no research investigated whether or to what extent different types of personality result in different levels of trust in ADS. This exploratory study asked 120 participants from around the world through an online questionnaire about their trust in ADS and assessed their personality, aimed at finding relations between personality traits and levels of trust in ADS.

Methods

Via an online crowd sourcing tool (Google CrowdSource), education platforms (university student association/notice boards), and social media (e.g., WhatsApp/Facebook), 120 participants from around the world filled out a questionnaire regarding trust in ADS. The survey included questionnaires on demographics, personality (Big Five Inventory; John et al. 1991; 2008), and trust in ADS (based on Jian and colleagues' [2000] questionnaire). Scores regarding level of trust were divided into five categories (very low to very high trust). A correlation analysis was performed for the Big Five Inventory and trust questionnaire scores per demographics variable.

Results

In total, 120 participants from 20 different countries (83 male, age M=27, SD=10) filled out the questionnaire. 20 participants did not have a driving license, and 68 were student. A moderate correlation was found where females scoring high on conscientiousness and those scoring low on neuroticism scored high on trust. Perhaps more interestingly, several correlations between trust and personality were found to score close to zero, meaning no correlation whatsoever. All demographics combined, openness and extraversion were least correlated to trust.

Conclusions

Although commonly thought that the average early adopter of automated driving systems are relatively old, wealthy males (see e.g., Hardman et al., 2019), our results were incapable of confirming this stereotype. Instead, automated driving systems appear to be trusted equally, regardless of the users' personality or demographics. Depsite being a relatively small, exploratory study, these results are promising, and should be expanded. Further research should go more in-depth, investigating other criteria of personality, demographics, and/or trust.