scholarly journals Comparison of Methods to Evaluate the Influence of an Automated Vehicle’s Driving Behavior on Pedestrians: Wizard of Oz, Virtual Reality, and Video

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 291 ◽  
Author(s):  
Tanja Fuest ◽  
Elisabeth Schmidt ◽  
Klaus Bengler
Keyword(s):  
Virtual Reality ◽  
Driving Behavior ◽  
Automated Vehicles ◽  
Recognition Time ◽  
Wizard Of Oz ◽  
Traffic System ◽  
Automated Vehicle ◽  
The Road ◽  
Road Users ◽  
Shared Space

Integrating automated vehicles into mixed traffic entails several challenges. Their driving behavior must be designed such that is understandable for all human road users, and that it ensures an efficient and safe traffic system. Previous studies investigated these issues, especially regarding the communication between automated vehicles and pedestrians. These studies used different methods, e.g., videos, virtual reality, or Wizard of Oz vehicles. However, the extent of transferability between these studies is still unknown. Therefore, we replicated the same study design in four different settings: two video, one virtual reality, and one Wizard of Oz setup. In the first video setup, videos from the virtual reality setup were used, while in the second setup, we filmed the Wizard of Oz vehicle. In all studies, participants stood at the roadside in a shared space. An automated vehicle approached from the left, using different driving profiles characterized by changing speed to communicate its intention to let the pedestrians cross the road. Participants were asked to recognize the intention of the automated vehicle and to press a button as soon as they realized this intention. Results revealed differences in the intention recognition time between the four study setups, as well as in the correct intention rate. The results from vehicle–pedestrian interaction studies published in recent years that used different study settings can therefore only be compared to each other to a limited extent.

scholarly journals Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment

2021 ◽  
Vol 13 (15) ◽  
pp. 8396
Author(s):  
Marc Wilbrink ◽  
Merle Lau ◽  
Johannes Illgner ◽  
Anna Schieben ◽  
Michael Oehl
Keyword(s):  
Urban Traffic ◽  
Automated Vehicles ◽  
Negative Effects ◽  
Communication Tools ◽  
Automated Vehicle ◽  
Additional Information ◽  
Road Users ◽  
Vulnerable Road Users ◽  
And Behaviors ◽  
Vehicle Automation

The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

Evaluation of Novel Urban Rail Level Crossing Designs Using Driving Simulation

2016 ◽  
Vol 60 (1) ◽  
pp. 1921-1925 ◽  
Author(s):  
G. J. M. Read ◽  
A. Clacy ◽  
M. Thomas ◽  
M. R. H. Van Mulken ◽  
N. Stevens ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Safety Concern ◽  
Driving Behavior ◽  
Subjective Ratings ◽  
Level Crossing ◽  
The Road ◽  
Design Concepts ◽  
Space Design ◽  
Shared Space ◽  
Urban Rail

Rail level crossings (RLXs) are a public safety concern internationally. The design of the RLX environment has been implicated in many recent crashes. In this study we evaluated three novel RLX design concepts using a driving simulator. Participants completed four drives, each incorporating one of the RLX designs (one baseline and three novel designs) in both train coming and train not coming mode. Measures of speed and braking on approach were analyzed, along with subjective measures of workload and usability. Superior driving behavior and subjective ratings were achieved for a design that incorporated an in-vehicle device while the lowest subjective ratings were given in relation to a shared space design that incorporated a simplified crossing environment and sharing of the road environment between motorized and non-motorized users. The implications for RLX safety are discussed.

scholarly journals Effects of Marking Automated Vehicles on Human Drivers on Highways

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 286 ◽  
Author(s):  
Tanja Fuest ◽  
Alexander Feierle ◽  
Elisabeth Schmidt ◽  
Klaus Bengler
Keyword(s):  
Road Traffic ◽  
Driving Behavior ◽  
Sensor Technology ◽  
Lane Change ◽  
Objective Data ◽  
Automated Vehicles ◽  
Traffic Jam ◽  
Automated Vehicle ◽  
Time Headway ◽  
First Time

Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle’s driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle’s driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans’ driving behavior would take place in the event of repeated encounters.

PIECEWISE FUNCTION FEEDBACK STRATEGY IN INTELLIGENT TRAFFIC SYSTEMS WITH A SPEED LIMIT BOTTLENECK

2011 ◽  
Vol 22 (08) ◽  
pp. 849-860 ◽  
Author(s):  
BOKUI CHEN ◽  
XIAOYAN SUN ◽  
HUA WEI ◽  
CHUANFEI DONG ◽  
BINGHONG WANG
Keyword(s):  
High Efficiency ◽  
Speed Limit ◽  
Traffic Patterns ◽  
Feedback Strategy ◽  
Traffic System ◽  
The Road ◽  
Road Users ◽  
Piecewise Function ◽  
Traffic Systems ◽  
Information Board

The road capacity can be greatly improved if an appropriate and effective information feedback strategy is adopted in the traffic system. In this paper, a strategy called piecewise function feedback strategy (PFFS) is introduced and applied into an asymmetrical two-route scenario with a speed limit bottleneck in which the dynamic information can be generated and displayed on the information board to guide road users to make a choice. Meanwhile, the velocity-dependent randomization (VDR) mechanism is adopted which can better reflect the dynamic behavior of vehicles in the system than NS mechanism. Simulation results adopting PFFS have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the previous strategies.

scholarly journals Pedestrians’ perceptions of automated vehicle movements and light-based eHMIs in real world conditions: A test track study

2021 ◽  
Author(s):  
Stefanie Horn ◽  
Ruth Madigan ◽  
Yee Mun Lee ◽  
Fabio Tango ◽  
Natasha Merat
Keyword(s):  
Real World ◽  
Test Track ◽  
Perceived Safety ◽  
Light Band ◽  
Automated Vehicle ◽  
The Road ◽  
Road Users ◽  
Human Machine Interfaces ◽  
Vulnerable Road Users ◽  
New Challenges

The development of increasingly automated vehicles (AVs) is likely to lead to new challenges around how they will interact with other road users. In the future, it is envisaged that AVs, manually driven vehicles, and vulnerable road users such as cyclists and pedestrians will need to share the road environment and interact with one another. This paper presents a test track study, funded by the H2020 interACT project, investigating pedestrians’ reactions towards an AV’s movement patterns and external Human Machine Interfaces (eHMIs). Twenty participants, standing on the side of a test-track road and facing an approaching AV, were asked to raise their arm to indicate: (1) when they could perceive the AV’s eHMI, which consisted of either a Full Light Band (FLB) or a Partial Light Band (PLB); (2) when they perceived the deceleration of the AV (with eHMI vs. no eHMI); and (3) when they felt safe to cross the road in front of the approaching AV (with eHMI vs. no eHMI). Statistical analyses revealed no effects of the presence of an eHMI on the pedestrians’ crossing decision or deceleration perception, but significant differences were found regarding the visibility of the FLB and PLB designs. The PLB design could be perceived at further distances than the FLB design. Both eHMI solutions were generally well-received, and participants provided high ratings of acceptance, perceived safety, and confidence around the AV.

scholarly journals Can Automated Vehicles Improve Cyclist Safety in Urban Areas?

Safety ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 57 ◽  
Author(s):  
Pavlos Tafidis ◽  
Ali Pirdavani ◽  
Tom Brijs ◽  
Haneen Farah
Keyword(s):  
Urban Areas ◽  
Road Traffic ◽  
Flow Simulation ◽  
Simulation Software ◽  
Assessment Model ◽  
Automated Vehicles ◽  
Passenger Cars ◽  
The Road ◽  
Road Users ◽  
Traffic Flow Simulation

Automated vehicles (AVs) are expected to assist in decreasing road traffic fatalities, particularly among passenger cars. However, until now limited research has been conducted on how they will impact the safety of vulnerable road users (VRUs) (i.e., cyclists and pedestrians). Therefore, there is a clear need to start taking into account the interactions between AVs and VRUs as an integrated element of the transport network, especially in urban areas where they are dominant. The objective of this study is to verify whether the anticipated implementation of AVs can actually improve cyclists’ safety. For this purpose, the microscopic traffic flow simulation software PTV Vissim combined with the surrogate safety assessment model (SSAM) were utilized. The road network used for this analysis was generated based on a real study case in a medium-sized city in Belgium, where narrow streets in the city center are shared on many occasions between vehicles and cyclists. The findings of the analysis show a notable reduction in the total number of conflicts between cars, but also between cars and cyclists, compared to the current situation, assuming a 100% market penetration scenario for AVs. Moreover, the severity level of conflicts also decreased as a result of the lack of human-driven vehicles in the traffic streams.

Testing the Effectiveness of Objective and Subjective Predictors of Driving Behavior under Fatigue

2019 ◽  
Vol 2673 (8) ◽  
pp. 343-352
Author(s):  
Giannis Adamos ◽  
Eftihia Nathanail
Keyword(s):  
Training Program ◽  
Driving Behavior ◽  
Severe Injuries ◽  
Subjective Data ◽  
Professional Drivers ◽  
The Road ◽  
Road Users ◽  
Before And After ◽  
Reported Data ◽  
The Impact

There is growing evidence that driver fatigue is a major road safety problem, causing crashes that frequently involve fatalities and severe injuries. Professional drivers are among the road users indicated by literature to be at high risk of involvement in a fatigue-related crash. The goal of this paper is to investigate the effectiveness of objective and subjective data collection and analysis in predicting driving behavior under fatigue. Toward this goal, the impact of a fatigue-training program addressing professional drivers was assessed through a naturalistic study on data collected by trip recorders (Geographical Positioning System devices). Analysis of these data was supplemented with the collection of self-reported data obtained through a questionnaire survey to investigate potential threats, deficiencies, and bias of the approaches. Findings revealed that there is some correlation between the two approaches measuring driving behavior under fatigue. Focusing on self-reported data, it was indicated that the training program affected positively the direction of change in the behavior of the professional drivers, addressed by an increase in the proportion of drivers who stop and rest when tired. The naturalistic approach and the testing of potential differences before and after the training program with reference to speed and average stop time showed that the training program encouraged drivers to reduce their speed and increase the time that they stop and rest.

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