Although automated driving systems have been used frequently, they are still unpopular in society. To increase the popularity of automated vehicles (AVs), assisting pedestrians to accurately understand the driving intentions and improving their perception of safety when interacting with AVs are considered effective. Therefore, the AV should send information about its driving intention to pedestrians when they interact with each other. However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for an AV to make pedestrians understand its driving intentions after being noticed by them? 2) What timing for an AV to make pedestrians feel safe after being noticed by them? Thirteen participants were invited to interact with a manually driven vehicle and an AV in an experiment. The participants' gaze information and a subjective evaluation of their understanding of the driving intention as well as their perception of safety were collected. By analyzing the participants' gaze duration on the vehicle with their subjective evaluations, we found that the AV should enable the pedestrian to accurately understand its driving intention within 0.5~6.5 [s] and make the pedestrian feel safe within 0.5~8.0 [s] while the pedestrian is gazing at it.
Interactions between pedestrians and automated vehicles (AVs) will increase significantly with the popularity of AV. However, pedestrians often have not enough trust on the AVs , particularly when they are confused about an AV's intention in a interaction. This study seeks to evaluate if pedestrians clearly understand the driving intentions of AVs in interactions and presents experimental research on the relationship between gaze behaviors of pedestrians and their understanding of the intentions of the AV. The hypothesis investigated in this study was that the less the pedestrian understands t…
Compared to a manual driving vehicle (MV), an automated driving vehicle lacks a way to communicate with the pedestrian through the driver when it interacts with the pedestrian because the driver usually does not participate in driving tasks. Thus, an external human machine interface (eHMI) can be viewed as a novel explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians when they need to negotiate in an interaction, e.g., an encountering scene. However, the eHMI may not guarantee that the pedestrians will fully recognize the intention o…
We use an immersive virtual reality environment to explore the intricate social cues that underlie non-verbal communication involved in a pedestrian's crossing decision. We "hack" non-verbal communication between pedestrian and vehicle by engineering a set of 15 vehicle trajectories, some of which follow social conventions and some that break them. By subverting social expectations of vehicle behavior we show that pedestrians may use vehicle kinematics to infer social intentions and not merely as the state of a moving object. We investigate human behavior in this virtual world by conducting a …
Department of Electrical Engineering and Computer Science, York University（约克大学电气工程与计算机科学系）；York University（约克大学）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Toronto（多伦多）；ON, Canada（加拿大安大略省）
One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between the vehicles and pedestrians as the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors such as demographics of the pedestrians, traffic dynamics, environmental conditions, etc. In this paper, we identify these factors by surveying pedestrian behavior studies,…
In this study, we investigated the effectiveness and user acceptance of three external interaction modalities (i.e., visual, auditory, and visual+auditory) in promoting communications between automated vehicle systems (AVS) and pedestrians at a crosswalk through a large number of combined designs. For this purpose, an online survey was designed and distributed to 68 participants. All participants reported their overall preferences for safety, comfort, trust, ease of understanding, usability, and acceptance towards the systems. Results showed that the visual+auditory interaction modality was th…
External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation
College of Transportation and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通学院和道路与交通工程教育部重点实验室）；Department of Civil and Environmental Engineering, National University of Singapore（新加坡国立大学土木与环境工程系）
eHMI for All -- Investigating the Effect of External Communication of Automated Vehicles on Pedestrians, Manual Drivers, and Cyclists in Virtual Reality
Institute of Media Informatics, Ulm University（乌尔姆大学媒体信息研究所）；UCL Interaction Centre（伦敦大学学院交互中心）；Eindhoven University of Technology（埃因霍温理工大学）
Occlusion-induced risk and interventions in pedestrian-autonomous truck interactions on multi-lane roads: A virtual reality study
University College London（伦敦大学学院）；Ningbo University（宁波大学）；The University of Hong Kong（香港大学）；Shanghai Maritime University（上海海事大学）；Tongji University（同济大学）
Wait or cross? Understanding the influence of behavioral tendency, trust, and risk perception on pedestrian gap-acceptance of automated truck platoons
Faculty of Maritime and Transportation, Ningbo University（宁波大学海运学院）；Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University（东南大学现代城市交通技术协同创新中心）；Centre for Transport Engineering and Modelling, Department of Civil and Environmental Engineering, Imperial College London（帝国理工学院土木与环境工程系交通工程与建模中心）；College of Transportation Engineering and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通运输工程学院及教育部道路与交通工程重点实验室）；School of Traffic and Transportation Engineering, Central South University（中南大学交通工程学院）
Seoul National University（首尔大学）；Dongguk University（东国大学）
Autonomous vehicles (AVs) can prevent collisions by understanding pedestrian intention. We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using machine learning approach, we classified head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that pedestrian head orientation change was influenced by AV yielding behavior as well as the size of the AV. Partic…
Autonomous Vehicles (AV) will transform transportation, but also the interaction between vehicles and pedestrians. In the absence of a driver, it is not clear how an AV can communicate its intention to pedestrians. One option is to use visual signals. To advance their design, we conduct four human-participant experiments and evaluate six representative AV visual signals for visibility, intuitiveness, persuasiveness, and usability at pedestrian crossings. Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and place…
Providing pedestrians and other vulnerable road users with a clear indication about a fully autonomous vehicle status and intentions is crucial to make them coexist. In the last few years, a variety of external interfaces have been proposed, leveraging different paradigms and technologies including vehicle-mounted devices (like LED panels), short-range on-road projections, and road infrastructure interfaces (e.g., special asphalts with embedded displays). These designs were experimented in different settings, using mockups, specially prepared vehicles, or virtual environments, with heterogeneo…
This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smart devices, is important context-information which can be used to parametrize subsequent algorithms, e.g. for dead reckoning or intention detection to improve the safety of vulnerable road users. The wearing location recognition can in terms of Organic Computing (OC) be seen as a step towards self-awareness and self-adaptation. For the wearing location detection a two-stage process is presented. It is subdivided into …
Institute of Industrial Science, the University of Tokyo（东京大学工业科学研究所）
Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can su…
