A Testing and Experimenting Environment for Microscopic Traffic Simulation Utilizing Virtual Reality and Augmented Reality

Microscopic traffic simulation (MTS) is the emulation of real-world traffic movements in a virtual environment with various traffic entities. Typically, the movements of the vehicles in MTS follow some predefined algorithms, e.g., car-following models, lane changing models, etc. Moreover, existing MTS models only provide a limited capability of two- and/or three-dimensional displays that often restrict the user’s viewpoint to a flat screen. Their downscaled scenes neither provide a realistic representation of the environment nor allow different users to simultaneously experience or interact with the simulation model from different perspectives. These limitations neither allow the traffic engineers to effectively disseminate their ideas to various stakeholders of different backgrounds nor allow the analysts to have realistic data about the vehicle or pedestrian movements. This dissertation intends to alleviate those issues by creating a framework and a prototype for a testing environment where MTS can have inputs from user-controlled vehicles and pedestrians to improve their traffic entity movement algorithms as well as have an immersive M3 (multi-mode, multi-perspective, multi-user) visualization of the simulation using Virtual Reality (VR) and Augmented Reality (AR) technologies. VR environments are created using highly realistic 3D models and environments. With modern game engines and hardware available on the market, these VR applications can provide a highly realistic and immersive experience for a user. Different experiments performed by real users in this study prove that utilizing VR technology for different traffic related experiments generated much more favorable results than the traditional displays. Moreover, using AR technologies for pedestrian studies is a novel approach that allows a user to walk in the real world and the simulation world at a one-to-one scale. This capability opens a whole new avenue of user experiment possibilities. On top of that, the in-environment communication chat system will allow researchers to perform different Advanced Driver Assistance System (ADAS) studies without ever needing to leave the simulation environment. Last but not least, the distributed nature of the framework enables users to participate from different geographic locations with their choice of display device (desktop, smartphone, VR, or AR). The prototype developed for this dissertation is readily available on a test webpage, and a user can easily download the prototype application without needing to install anything. The user also can run the remote MTS server and then connect their client application to the server

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Introduction: In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers' performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives: The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology: First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers' engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions: Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver's quick intervention. (c) 2021, The Author(s)

Virtual Reality Based Simulation Testbed for Evaluation of Autonomous Vehicle Behavior Algorithms

Validation of Autonomous Vehicle behavior algorithms requires thorough testing against a wide range of test scenarios. It is not financially and practically feasible to conduct these tests entirely in a real world setting. We discuss the design and implementation of a VR based simulation testbed that allows such testing to be conducted virtually, linking a computer-generated environment to the system running the autonomous vehicle\u27s decision making algorithms and operating in real-time. We illustrate the system by further discussing the design and implementation of an application that builds upon the VR simulation testbed to visually evaluate the performance of an Advance Driver Assist System (ADAS), namely Cooperative Adaptive Cruise Control (CACC) controller against an actor using vehicular navigation data from real traffic within a virtual 3D environment of Clemson University\u27s campus. With this application, our goal is to enable the user to achieve spatial awareness and immersion of physically being inside a test car within a realistic traffic scenario in a safe, inexpensive and repeatable manner in Virtual Reality. Finally, we evaluate the performance of our simulator application and conduct a user study to assess its usability

Driver behaviour with adaptive cruise control

This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts

Two Wheelistic: Development of a High-Fidelity Virtual Reality Cycling Simulator for Transportation Safety Research

This thesis presents the development of an immersive, high-fidelity virtual reality (VR) cycling simulator, where one can ride a stationary bicycle in a simulated virtual environment and interact with other road users (e.g., drivers). Inspired by driving simulators, a VR cycling simulator has potential to become a valuable tool for conducting traffic safety research involving bicyclists. The hardware and software development and integration were described in detail as a reference for others that may want to build similar systems. The VR simulation includes a representation of a real-world urban environment with a road network, and utilizes a VR headset coupled with an appropriate stationary bike system setup. The first phase of development was focused on an immersive, interactive simulator, in which users are able to control their movements within the virtual environment. They control their speed by pedaling the stationary bike and can steer using buttons on the handlebar-mounted controllers. This aspect of the simulator is instrumental in applications that require observing participants' cycling behaviors in a safe, virtual environment. Validation was performed to ensure the cycling simulator functioned at realistic speeds and in accordance with the user's input. The second development phase focused on a use case for driver education and training, presented through a variety of common dangerous bicyclist encounters programmed into VR scenarios. Drivers who have limited bicycling experience can experience these scenarios in a safe, virtual setting to better understand the bicyclist's perspective.MSHuman-Centered Design and Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/167360/1/Ayah Hamad - Final Thesis - Two Wheelistic.pd

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants. As part of the iterative development process, it is necessary to detect criticalscenarios and generate knowledge from them to improve the highly automated driving (HAD) function. In order to tackle this challenge,numerous datasets have been released in the past years, which act as the basis for the development and testing of such algorithms.Nevertheless, the remaining challenges are to find relevant scenes, such as safety-critical corner cases, in these datasets and tounderstand them completely.Therefore, this paper presents a methodology to process and analyze naturalistic motion datasets in two ways: On the one hand, ourapproach maps scenes of the datasets to a generic semantic scene graph which allows for a high-level and objective analysis. Here,arbitrary criticality measures, e.g. TTC, RSS or SFF, can be set to automatically detect critical scenarios between traffic participants.On the other hand, the scenarios are recreated in a realistic virtual reality (VR) environment, which allows for a subjective close-upanalysis from multiple, interactive perspectives.Comment: Accepted for publication at ICITE 202

The effects of instruction and environmental demand on state anxiety, driving performance and autonomic activity: Are ego-threatening manipulations effective?

A small yet emerging body of research on the relationship between anxiety and driving suggests that higher levels of state anxiety may lead to more dangerous driving behaviours. The aim of the current research was to investigate the effects of increased state anxiety on driving behaviours within a simulated environment using instructional sets to manipulate anxiety levels. In Study One, whilst a set of safety-related instructions were able to increase state anxiety, this did not result in changes to driving behaviours. In Study Two, ego-threatening instructions were not able to successfully increase state anxiety. This has implications regarding instructional sets in research, including their task relevance and the necessity for a motivational incentive. However, when changes in anxiety were considered regardless of instruction group, Study Two found changes in SDLP and skin conductance levels related to state anxiety increases. As these effects were context specific, it is argued that some of these changes may be due to poorer processing efficiency, leading to suggestions about the types of behaviours that may need to be trained in potential therapies for those who show high state anxiety levels whilst driving