skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Developing Empathy Among Drivers to Improve Safety in Highway Construction Work Zones
Temporary traffic control (TTC) in highway work zones has significant implications and challenges in terms of safety for road users and workers. Work zone workers are increasingly concerned about the risks they face due to their proximity to live traffic on the road. Drivers tend to be less aware of the risks faced by workers in highway work zones. The public should develop empathy to increase awareness about the danger construction workers are exposed in highway work zones. The research objective was to use virtual reality (VR) with a role-playing situation with almost complete sensory immersion in a controlled environment and a driving simulator to investigate if exposing drivers to the work hazards that highway construction workers typically encounter influences their behaviour while driving through work zones. The study compared the driving behaviours in the simulator between subjects sensitized using VR to the subjects who were not sensitized using VR. The simulation included the use of a GPS device that instructed drivers to turn on a road that was blocked by the TTC of the work zone as a distraction strategy. The results indicate that participants exposed to VR made safer driving decisions than participants without the VR intervention. The results suggest that drivers' empathy towards highway construction workers in a work zone can positively impact safety, communication, and well-being.  more » « less
Award ID(s):
1832468
PAR ID:
10468678
Author(s) / Creator(s):
Publisher / Repository:
XXII Conferencia Panamericana de Ingeniería y Logística de Tránsito y Transporte
Date Published:
Subject(s) / Keyword(s):
highway safety, empathy, virtual reality, highway work zones, driving simulator
Format(s):
Medium: X
Location:
Guayaquil, Ecuador
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , Transportation Research Record: Journal of the Transportation Research Board)
    null (Ed.)
    Given the aging infrastructure and the anticipated growing number of highway work zones in the U.S.A., it is important to investigate work zone merge control, which is critical for improving work zone safety and capacity. This paper proposes and evaluates a novel highway work zone merge control strategy based on cooperative driving behavior enabled by artificial intelligence. The proposed method assumes that all vehicles are fully automated, connected, and cooperative. It inserts two metering zones in the open lane to make space for merging vehicles in the closed lane. In addition, each vehicle in the closed lane learns how to adjust its longitudinal position optimally to find a safe gap in the open lane using an off-policy soft actor critic reinforcement learning (RL) algorithm, considering its surrounding traffic conditions. The learning results are captured in convolutional neural networks and used to control individual vehicles in the testing phase. By adding the metering zones and taking the locations, speeds, and accelerations of surrounding vehicles into account, cooperation among vehicles is implicitly considered. This RL-based model is trained and evaluated using a microscopic traffic simulator. The results show that this cooperative RL-based merge control significantly outperforms popular strategies such as late merge and early merge in terms of both mobility and safety measures. It also performs better than a strategy assuming all vehicles are equipped with cooperative adaptive cruise control. 
    more » « less
  2. ; ( , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows. 
    more » « less
  3. ; ; ; ( , EPiC Series in Built Environment)
    Integrating drones into construction sites can introduce new risks to workers who already work in hazardous environments. Consequently, several recent studies have investigated the safety challenges and solutions associated with this technology integration in construction. However, there is a knowledge gap about effectively communicating such safety challenges to construction professionals and students who may work alongside drones on job sites. In this study, a 360-degree virtual reality (VR) environment was created as a training platform to communicate the safety challenges of worker-drone interactions on construction jobsites. This pilot study assesses the learning effectiveness and user experience of the developed 360 VR worker-drone safety training, which provides an immersive device-agnostic learning experience. The result indicates that such 360 VR learning material could significantly increase the safety knowledge of users while delivering an acceptable user experience in most of its assessment criteria. The outcomes of this study will serve as a valuable resource for improving future worker-drone safety training materials. 
    more » « less
  4. ; ; ( , ACM/IEEE International Conference on Human-Robot Interaction)
    Simulators are an essential tool for behavioural and interaction research on driving, due to the safety, cost, and experimental control issues of on-road driving experiments. The most advanced simulators use expensive 360 degree projections systems to ensure visual fidelity, full field of view, and immersion. However, similar visual fidelity can be achieved affordably using a virtual reality (VR) based visual interface. We present DReyeVR, an open-source VR based driving simulator platform designed with behavioural and interaction research priorities in mind. DReyeVR (read ''driver'') is based on Unreal Engine and the CARLA autonomous vehicle simulator and has features such as eye tracking, a functional driving heads-up display (HUD) and vehicle audio, custom definable routes and traffic scenarios, experimental logging, replay capabilities, and compatibility with ROS. We describe the hardware required to deploy this simulator for under 5000 USD, much cheaper than commercially available simulators. Finally, we describe how DReyeVR may be leveraged to answer an interaction research question in an example scenario. DReyeVR is open-source at this url: https://github.com/HARPLab/DReyeVR 
    more » « less
  5. ; ( , The 10th International Cycling Safety Conference 2022)
    Cycling as a mode of transportation has been recording an upward trend in both the U.S. and Europe. Unfortunately, the safety of cyclists has been a point of growing concern. Data from the National Highway Traffic Safety Administration (NHTSA) show that the crashes that occur during the events of motorists overtaking cyclists was one of the leading categories involving cyclists in fatal crashes. In support of the efforts to understand the driving behavior of drivers of motorized vehicles while overtaking cyclists, this research project is aimed at developing an algorithm to identify the overtaking events. Most existing quantitative studies on cycling safety leverage instrumented bicycles or vehicles with sensors for extracting naturalistic driving trajectories. Whereas we use data from a recent research that provides naturalistic driving trajectories of road users collected at select intersections in urban areas in Germany using drones equipped with cameras. Using these videos with a data frequency of 25 Hz, the authors of this study have output inD dataset. The inD dataset contains trajectories of road users that are captured in form of coordinates on a two-dimensional plane obtained from the ariel or bird's eye view of the road. Additionally, the data also captures velocity, acceleration, heading angles, dimensions of driver's vehicle etc. Overtaking can be thought of as four phases of approaching, steering away, passing, and returning. Using the inD dataset, we have developed an algorithm to identify events when a driver of motor vehicle overtakes a cyclist. This work fits into our broader goal to contribute to the body of knowledge for improving road safety of cyclists. The work is expected to provide inputs to governmental/ traffic authorities in aspects such as design of intersections and design of bicycle lanes by providing insights into overtaking events. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email