Pedestrian-AV interactions at unmarked midblock: Effects of eHMI onset timing and vehicle kinematics on young adult pedestrian behavior and subjective safety perception.

Highly automated vehicles (HAVs) will soon be introduced into mixed urban traffic. Pedestrians might have an idea of HAVs. Nevertheless, they probably have never interacted with them before. Moreover, pedestrians will not be able to communicate with HAVs like they are used to with manual vehicles. External human–machine interfaces (eHMIs) are possible design solutions for HAVs to ensure safe interaction with other road users. Light-based eHMIs positively affected pedestrians’ trust ratings, perceived safety, and willingness to cross. However, previous studies often neglected the effect of vehicle size, although larger-sized HAVs could be potentially perceived as the more significant threat. Additionally, the relationship between vehicle kinematics and eHMIs for differently sized HAVs remains an underexplored research topic. This study investigated the effects of vehicle size (small vs. large), eHMI state (dynamic eHMI vs. static eHMI vs. no eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrian crossing behavior and their subjective assessment. In virtual reality, we created a shared space traffic scenario, in which the eHMI and vehicle kinematics matched or did not match. For yielding conditions, the results showed that participants felt more aroused with larger HAVs than with smaller HAVs. Moreover, pedestrians initiated their crossing significantly earlier when both vehicle sizes had a dynamic eHMI compared to a static eHMI vs. no eHMI. Additionally, pedestrians evaluated a dynamic eHMI with higher trust ratings, higher perceived safety, and more positive affective reactions. The results manifested that the use of dynamic eHMIs can effectively enhance pedestrian-vehicle communication with a large and a small HAV. For non-matching conditions, the participants tended to rely on the vehicle kinematics for both vehicle sizes. Overall, the study highlighted the potential of eHMIs for pedestrian interactions with HAVs of varying sizes when they are well-coordinated with the vehicle kinematics, aiming to enhance safety and efficiency in mixed-traffic environments.

Uncovering pedestrian stress and crossing strategies via mixed-reality encounters with autonomous vehicles.

Defensive or competitive Autonomous Vehicles: Which one interacts safely and efficiently with pedestrians?

Future automated vehicles (AVs) of different sizes will share the same space with other road users, e. g., pedestrians. For a safe interaction, successful communication needs to be ensured, in particular, with vulnerable road users, such as pedestrians. Two possible communication means exist for AVs: vehicle kinematics for implicit communication and external human-machine interfaces (eHMIs) for explicit communication. However, the exact interplay is not sufficiently studied yet for pedestrians' interactions with AVs. Additionally, very few other studies focused on the interplay of vehicle kinematics and eHMI for pedestrians' interaction with differently sized AVs, although the precise coordination is decisive to support the communication with pedestrians. Therefore, this study focused on how the interplay of vehicle kinematics and eHMI affects pedestrians' willingness to cross, trust and perceived safety for the interaction with two differently sized AVs (smaller AV vs. larger AV). In this experimental online study (N = 149), the participants interacted with the AVs in a shared space. Both AVs were equipped with a 360° LED light-band eHMI attached to the outer vehicle body. Three eHMI statuses (no eHMI, static eHMI, and dynamic eHMI) were displayed. The vehicle kinematics were varied at two levels (non-yielding vs. yielding). Moreover, “non-matching” conditions were included for both AVs in which the dynamic eHMI falsely communicated a yielding intent although the vehicle did not yield. Overall, results showed that pedestrians' willingness to cross was significantly higher for the smaller AV compared to the larger AV. Regarding the interplay of vehicle kinematics and eHMI, results indicated that a dynamic eHMI increased pedestrians' perceived safety when the vehicle yielded. When the vehicle did not yield, pedestrians' perceived safety still increased for the dynamic eHMI compared to the static eHMI and no eHMI. The findings of this study demonstrated possible negative effects of eHMIs when they did not match the vehicle kinematics. Further implications for a holistic communication strategy for differently sized AVs will be discussed.

Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

Road-crossing behavior and safety of pedestrians facing autonomous vehicles with an acceleration indicator eHMI in VR traffic flow

The objective of this work was to better understand pedestrians' understanding, trust, comfort, and acceptance of autonomous vehicle (AV) external human-machine interfaces (eHMIs). A link between mechanical engineering (i.e., automotive engineering) and civil engineering (i.e., multimodal transportation systems) is necessary to understand the effectiveness of varying AV-to-human communication strategies. Using a within-subject experiment design, 47 participants interacted with AVs possessing one of four eHMIs in a virtual reality (VR) environment. We administered a Likert scale survey to measure participants' perceptions of the eHMIs and used ordinal logistic regressions to analyze the results. We also accounted for participants' gender and stated interest in AVs, novel contributions to this field of research. The presence of an eHMI was found to improve participants' perceptions of AVs. Although females generally reported higher levels of understanding, trust, comfort, and acceptance, males' scores increased more significantly with the introduction of an eHMI. Text eHMIs outperformed nontextual interfaces, with participants noting the best perceptions with the text eHMI located on the AV's grille. Participants' understanding and identification of right-of-way (ROW) were most improved by the eHMIs while trust and comfort were most impacted by the participants' stated interest in AVs. Acceptance had little response to the eHMIs or stated AV interest and gender had little impact in the statistical models. This research supports the development of a standard, uniform AV-pedestrian communication strategy and strengthens the connection between humans and AVs.

As autonomous vehicles become more prevalent in urban traffic settings, the safety of vulnerable road users, predominantly pedestrians, must be placed in high regard relative to the design of the autonomous vehicle's (AV's) external human machine interface (HMI). Traditionally, there exist communication methods between drivers and pedestrians, such as hand gestures, eye contact, and verbal cues that convey the driver's awareness of the pedestrian's presence. However, with autonomous vehicles, there is a shift in communicative responsibility from the driver to the vehicle itself. It is the vehicle's responsibility to intuitively and clearly indicate its actions to the pedestrian. This research analyzes the factors contributing to AV skepticism and the ways in which the visual aspect of an AV's external HMI can be improved from traditional vehicle designs to accommodate visually impaired pedestrians. This was achieved by performing a study on 27 participants varying in age, gender, and vision impairment type. The study includes a survey and interview portion. Findings indicate that yellow and blue colors are viewed as most welcoming and memorable. It is suggested that these colors be used in the projected light system of the external HMI design. Quantitative results indicate that there is a moderate degree of correlation between the following: the use of cruise control and vision impairment severity (negative correlation), a participant's willingness to ride in an AV and vision impairment levels (positive correlation). The study also found a low degree of correlation in a participants willingness to ride in an AV and their trust in AVs. Based on these findings and under the assumption than an external HMI is needed on the AV, it is recommended that the external HMI contain a light projection system on the vehicle's front body. Based on qualitative results, the light projection system should use a teal color light and project a directional arrow onto the ground when identifying a pedestrian in its path while turning. Intuitive signals such as these help ensure pedestrian safety and promote trust and acceptance of the use of autonomous vehicles on public roads.

The advent of autonomous vehicles (AVs) has sparked many concerns about pedestrian safety, prompting manufacturers and researchers to integrate external Human–Machine Interfaces (eHMIs) into AVs as communication tools between vehicles and pedestrians. The evolving dynamics of vehicle–pedestrian interactions make eHMIs a compelling strategy for enhancing safety. This study aimed to examine the contribution of eHMIs to safety while exploring the impact of an incentive system on pedestrian risk behavior. Participants interacted with AVs equipped with eHMIs in an immersive environment featuring two distinct scenarios, each highlighting a sense of urgency to reach their destination. In the first scenario, participants behaved naturally without specific instructions, while in the second scenario, they were informed of an incentive aimed at motivating them to cross the road promptly. This innovative experimental approach explored whether motivated participants could maintain focus and accurately perceive genuine risk within virtual environments. The introduction of a reward system significantly increased road-crossings, particularly when the vehicle was approaching at higher speeds, indicating that incentives encouraged participants to take more risks while crossing. Additionally, eHMIs notably impacted pedestrian risk behavior, with participants more likely to cross when the vehicle signaled it would not stop.

Ninety-five percent of all roadway crashes are attributed fully or partially to human error, and a multitude of safety-related programs, policies, and initiatives have seen limited success in reducing roadway crashes and their accompanying fatalities, injuries, and property damage. For this reason, safety professionals have lauded the emergence of autonomous vehicles (AVs) as a promising palliative to the persistent problem of road crashes. Such optimism is reflected in recent literature that have argues from a conceptual standpoint, that road safety enhancement will be one of the prospective benefits of AV operations because automation removes humans from vehicle driving operations and therefore criminates or mitigates human error. It can be argued that the safety benefits of AVs will be manifest when AV market penetration reaches 100%. However, it seems clear from a practical standpoint that the transition from a system of exclusively human-driven vehicles (HDVs) to that of exclusively AVs will not only be necessary but also an arduous journey. This transition period will be characterized by heterogeneous traffic, where human-driven vehicles (HDVs) and AVs share the road space, and whence the prospective safety benefits of AVs may not be fully realized due to human error arising from the HDV operations in the mixed traffic space. These traffic conflicts, which may lead to collisions, could arise from any of several contexts of driving maneuvers, one of which is aggressive lane changes, the focus of this thesis. From the literature, it is clear that lane changing is inherently more collision-prone compared to most other maneuvers including car following, and therefore the consequences of errant human driving behavior such as inattention of misjudgment during lane changing, are more severe. To address this problem, this thesis developed a control framework to be used by AVs to help them avoid collision in a mixed traffic stream with human drivers who exhibit aggressive lane-changing behavior. The developed framework, which is based on a Model Predictive Control (MPC) approach, is designed to control the AV’s movements safely by duly accommodating potential human error from the HDVs that could otherwise lead to any of two common collision patterns: rear-end and side-impact. Further, the thesis investigated how connectivity between the HDVs, and AVs could facilitate joint operational decision-making and sharing of real-time information, thereby further enhancing the safety of the entire traffic stream. Finally, the thesis presents the results of driving simulations carried out to test and validate the performance of the control framework under different traffic conditions.

In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.

The Effect of Autonomous Vehicles on Consumer Welfare in Ride-Hailing Markets

When tasks are allocated dynamically within a human-computer system, system performance depends upon effective communication between the human and the computer. The results are presented of simulation studies which investigate two means of human-to-computer communication: implicit communication, in which the human's planned actions are conveyed to the computer through a model of the human's action strategy; and explicit communication, in which the human overtly transmits decisions to the computer. The results indicate that the effectiveness of implicit communication depends upon both the predictive validity of the model employed and the computer task allocation strategy built upon the model. The effectiveness of explicit communication depends upon the time the human must devote to transmitting action plans to the computer. These simulations indicate the payoffs that can be realized by optimization of implicit and explicit human-computer communication. They also provide an efficient means to determine the appropriate mix of these communication techniques for a given situation. Perhaps the most interesting finding is that implicit communication based upon a model possessing only modest predictive validity is capable of enhancing system performance. This suggests that implementation of implicit communication within human-computer systems need not require the development and use of complex all-encompassing models of human performance.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.