How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections?

Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T-intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T-intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.     

Car-following behavioural adaptation when driving next to automated vehicles on a dedicated lane on motorways: A driving simulator study in the Netherlands

Automated vehicles (AVs) are expected to improve traffic flow efficiency and safety. The deployment of AVs on motorways is expected to be the first step in their implementation. One of the main concerns is how human drivers will interact with AVs. Dedicating specific lanes to AVs have been suggested as a possible solution. However, there is still a lack of evidence-based research on the consequence of dedicated lanes for AVs on human drivers’ behavior. To bridge this research gap, a driving simulator experiment was conducted to investigate the behavior of human drivers exposed to different road design configurations of dedicated lanes on motorways. The experiment sample consisted of 34 (13 female) licensed drivers in the age range of 20–30. A repeated measures ANOVA was applied, which revealed that the type of separation between the dedicated lane and the other lanes has a significant influence on the behavior of human drivers driving in the proximity of AV platoons. Human drivers maintained a significantly lower time headway (THW) when driving in the proximity of a continuous access dedicated lane as compared to a limited-access dedicated lane with a guardrail separation for AV platoons. A similar result was found for the limited-access dedicated lane in comparison to the limited-access dedicated lane with guardrail separation. Moreover, the results regarding the empirical relationships between THW and sociodemographic variables indicate a significant THW difference between males and females as well as a significant inverse relationship between THW and the years of driving experience.     

Exploring the acceptance of connected and automated vehicles: Focus group discussions with experts and non-experts in transport

The successful deployment of connected and automated vehicles (CAVs) will largely depend on public opinion. Expectations and concerns are essential components driving the speed of market uptake and final adoption of these technologies. We have studied user expectations and concerns, as well as their provenance, by conducting a series of Focus Group (FG) discussions with transport area experts and non-expert participants, in Italy, Germany, and Spain. Together with user acceptance, potential advantages and disadvantages that CAVs may bring at a societal level were explored. Results show that benefits like increased safety and accessibility and improved travel experience for both driver and passengers could support CAVs’ deployment. Nevertheless, a variety of concerns were raised during the discussions, related to privacy, responsibility in case of an accident, and increase in maintenance and repair costs. These aspects could significantly hamper or delay CAVs acceptability and need to be carefully considered by stakeholders. The reported insights from the FG discussions and the fact that some participants noted positive changes of their initial idea of CAVs due to their participation in the discussion, exemplify the importance of citizen engagement activities to address mobility challenges.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.     

Exploring behavioral validity of driving simulator under time pressure driving conditions of professional drivers

Driving simulators have become an important tool in human factors research, given that they are appropriately validated. Therefore, this study aims to explore the behavioral (absolute and relative) validity of a fixed-base driving simulator by analyzing different driving behavior measures such as speed, longitudinal acceleration, lateral acceleration, and brake pedal force. Thirty professional drivers participated in the experiment and the data was collected in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Initially, comparative analyses were conducted on different driving behavior measures using Wilcoxon-signed rank test to examine absolute validity of the driving simulator. Finally, Generalized Linear Mixed (GLM) models were developed for computing the effective distance between real and simulated worlds by quantifying the parameters and for establishing relative validity. In general, the continuous profiles of driving behavior measures followed similar trends in real and simulated worlds and comparative analyses indicated relative validity of the driving simulator. The GLM models showed significant interaction effect of driving environments (real-world and simulated world) and driving conditions (NTP and TP) where high driving speed, high brake pedal force, and low lateral acceleration were observed in simulated world under TP driving condition than real-world under TP driving condition. Overall, the statistical analyses showed qualitative correspondence (relative validity) of the driving behavior measures in between real and simulated worlds. The findings from the current study showed expediency of the driving simulator and its effectiveness in conducting research on human factors and driver safety.     

Psychometric properties of driver self-image inventory for Chinese drivers and its associations with personality and driving style

This study aimed to adapt the Driver Self-image Inventory (DSII, Taubman-Ben-Ari, 2008) to Chinese drivers and examine its relationship with personality traits and driving style. Six hundred forty drivers aged 18–55 years agreed to participate in this study. Measurements included the DSII, a personality scale and a validated Chinese version of the Multidimensional Driving Style Inventory (MDSI). The results of exploratory factor analysis (n = 302) and confirmatory factor analysis (n = 305) yielded a three-factor scale with satisfactory reliability. Significant gender differences were found on the DSII factors, with male drivers scoring higher on the impulsive driver factor and lower on the cautious driver factor than female drivers. The validity of the DSII was supported by significant associations between the DSII factors and personality traits, driving style and number of traffic violations and accidents in the previous 12 months. Moreover, drivers with traffic accidents scored significantly lower on the cautious driver factor and higher on the impulsive driver factor than those without traffic accidents. These findings indicate that the reliability and validity of the Chinese version of the DSII are acceptable.     

Effects of mode distinction,user visibility,and vehicle appearance on mode confusion when interacting with highly automated vehicles

Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle’s communication. To evaluate potential solutions to this problem, we conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tintable windshield. Our results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tintable windshield, however, was negatively assessed. Thus, our work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion.     

Moral disengagement mechanisms in interactions of human drivers with autonomous vehicles: Validation of a new scale and relevance with personality,driving style and attitudes

The introduction of autonomous vehicles (AVs) in the road transportation systems raises questions with respect to their interactions with human drivers’, especially during the early stages. Issues such as unfamiliarity or false assumptions regarding the timid and safe behaviour of AVs could potentially result in undesirable human driver behaviours, for instance “testing” AVs or being aggressive towards them. Among other factors, morality has been determined as a source of aggressive driving behaviour. Following previous approaches on moral disengagement, the current paper argues that moral standards during interactions of human drivers with AVs could potentially blur, leading to the disengagement of self-regulation mechanisms of moral behaviour. The study investigates the impact of moral disengagement on the intention of human drivers to be aggressive towards AVs. To that end, an online survey was conducted including a newly developed survey of moral disengagement, adapted to the context of AVs. Moreover, measures of personality, driving style, attitudes towards sharing the road with AVs and perceived threats were collected. A confirmatory factor analysis provided support for the concept of moral disengagement in the context of AVs. Moreover, relationships between personality, driving style and attitudes towards sharing the road with AVs were found, via a structural equation modelling approach (SEM). The results could have implications in the future driver training and education programmes, as it might be necessary to not only focus on driving skills but also on the development of procedural skills that will improve the understanding of AVs’ capabilities and ensure safer interactions. Efforts on improving attitudes towards AVs may also be necessary for improving human driver behaviour.