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It is envisioned that Connected and Automated Vehicles (CAVs) are the future of transportation as they can assist in minimizing some inefficiencies with the current transport systems. However, it is not clear how drivers of conventional vehicles would interact with CAVs in a mixed traffic environment containing both CAVs and human driven vehicles (HDVs). Thus, this study aims to investigate drivers’ behaviors towards CAVs through driving simulation experiment and national survey study. Two on-ramp and two off-ramp driving simulation scenarios were designed where drivers were asked to merge with two-lane highway in presence of HDVs and CAVs truck platoon in the on-ramp scenarios. In the two off-ramp scenarios, they were asked to take exit in presence of HDVs and CAVs truck platoon in the lane to their right. A before-after survey was conducted among the participant of the driving simulation experiment and an online survey was conducted to investigate their opinion in different traffic, road and environmental condition in presence of CAVs. Furthermore, two driving simulation scenarios were designed to test drivers’ behaviors during automated driving mode and their reaction when the control was shifted to the manual driving mode. Results from the on-ramp scenarios and off-ramp scenarios indicate that more than half of the drivers preferred to merge in front of CAV truck platoon and around two-third of the drivers chose to diverge behind the platoon. The online survey revealed that around two-third of the respondents would not overtake CAV platoon in two-lane two-way road, whereas around 60% would do so in case of three lane highway. During automation failure, drivers demonstrated lower take-over reaction time (TORt), lower deceleration and higher TTC in scenarios with non-driving related tasks (NDRT) compared to the scenarios with no NDRT (manual mode). The before-after survey results suggest that most of the drivers found the navigation with CAV easier after participating in the experiment.


Tran-SET Project: 21ITSLSU16