Safe-D: Safety through Disruption

Projects: Automated Vehicles


Human Factors of Level 3 Automation: Surprise Event Response Evaluation

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Abstract Level 3 automation is the first automation level that turns over supervising the driving task to the vehicle. The extent that this capability will be wanted and used by drivers is likely driven at least in part by the driver’s ability and desire to do non-driving tasks for extended periods of time. This project […]

Curb Management Practices and Effectiveness in Improving Safety

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Abstract The research project will address how vehicles in a multimodal environment are managed and prioritized at curb loading and unloading zones between different public and private vehicles and/or use cases. The research will analyze the effectiveness of curb management practices in improving safety through reduced collisions with pedestrians and other vehicles. The research will […]

Evaluation Tools for Automated Shuttle Transit Readiness of the Area

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Abstract This project aims to develop a general evaluation protocol for transit readiness in the area for Automated shuttle implementation. Using the data gathered from the EasyMile shuttle implemented in Fairfax County, Virginia, the research team will perform risk assessments and safety analysis for the automated shuttle to understand the risks associated with the interactions […]

ENDEAVRide

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Abstract In fall 2020, a novel autonomous-vehicle (AV) service named ENDEAVRide will start pilot testing in Nolanville, a typical rural town in central Texas. The AV will serve as a taxicab and a mobile telemedicine portal. This project marks the first attempt to conduct a real-world assessment of AV’s potential safety impacts as a disruptive […]

Lane Change Hazard Analysis Using Radar Traces to Identify Conflicts and Time-To-Collision

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Abstract This project will mine an existing set of radar data surrounding real-world lane change events executed by drivers relying on both conventional mirror and camera-based systems. The data set provides valuable opportunities to develop computer-based algorithms for dealing with and managing radar traces to identify normative lane change signatures as well as conflict-based events […]

Cooperative Perception of Connected Vehicles for Safety

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Abstract This project develops vision-based cooperative perception and accident (crash) avoidance trajectory plans in dynamic environments for two connected vehicles in which the ego vehicle would face a potentially unseen hazard ahead but could receive safety-critical information from a vehicle in front and estimate/predict the trajectory of the potential hazard. There are several challenging technical […]

A data driven approach to the development and evaluation of acoustic electric vehicle alerting systems for vision impaired pedestrians

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Abstract The steady increase of electric vehicles (EVs) has led to safety concerns for vulnerable populations. EVs produce considerably less noise compared to the internal combustion engine (ICE) vehicles, especially at low speeds. Although pedestrians across all demographics are at risk, visually impaired pedestrians face significantly greater disadvantages in environments where ambient noise levels are […]

Improving Methods to Measure Attentiveness through Driver Monitoring

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Abstract Distracted drivers are involved in approximately 4 million vehicle accidents each year in the U.S. (Dingus et al. 2016). These crashes result in many lives lost and billions of dollars in damages. This widespread issue has resulted in the adoption of regulations in the European Union that will require all new vehicles produced by mid-2022 to be equipped with driver monitoring […]

Investigating and Developing Methods for Traditional Participant-based Data Collection with Remote Experimenters

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Abstract The research team will investigate and develop methods and technologies that would allow experimenters to conduct and monitor data collection from a remote location. Although it is often required for researchers to supervise experiments, physically removing them from the vehicle can increase realism and offer naturalistic observations in traditional, experimenter-conducted studies. We expect this […]

Automated Shuttles and Buses for All Users

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Abstract Individuals using wheelchairs and those with limited or no sight face extra safety issues in the use of public transit, as well as personal vehicles, including getting to and from a bus or shuttle stop, getting on and off a vehicle, and being secure while riding in a vehicle. The demonstrations of automated shuttles […]

Autonomous Delivery Vehicle as a Disruptive Technology: How to Shape the Future with a Focus on Safety?

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  Abstract The National Highway Traffic Safety Administration (NHTSA) recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs). Unlike conventional low-speed vehicles, these ADVs are designed to have no human occupants and they operate exclusively using an automated driving system. However, extensive safety-related issues of these vehicles have not been examined. With the enormous […]

Crashworthiness Compatibility Investigation of Autonomous Vehicles with Current Passenger Vehicles

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Abstract This project will propose testing and evaluation criteria to investigate crash compatibility between autonomous and human-driven vehicles, with consideration of different potential crash scenarios. Finite element computer models will then be utilized to conduct predictive simulations investigating potential cases of impacts between human-driven and autonomous vehicles. Current regulations defining IIHS testing criteria will be […]

Behavioral Indicators of Drowsy Driving: Active Search Mirror Checks

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Abstract Driver impairment, due to drowsiness or fatigue, has a significant impact on the safety of all road users. Assessing an impairment such as driver drowsiness, through the use of vehicle-based technology, continues to be an area of interest. Both the initial detection, as well as continued monitoring, of driver drowsiness have been the emphasis […]

Prediction of Vehicle Trajectories at Intersections Using Inverse Reinforcement Learning

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The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems which aim to identify critical events such as near crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or the infrastructure to avoid or mitigate potential crashes.

Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data

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This project w​ill contribute to connected vehicles and ADS real-time safety monitoring, NDS data analysis, hail-driving driver safety prediction, as well as fleet and driver safety management programs.​​​​​​​

Impact of Automated Vehicle External Communication on Other Road User Behavior

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Road users communicate with one another in various ways (e.g. hand gestures, eye contact). There is growing concern in the industry about whether highly automated vehicles (HAVs) will be able to communicate intent to other road users in the same ways, and to the same benefits, that a human can.

Radar and LiDAR Fusion for Scaled Vehicle Sensing

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This project proposes a sensor fusion approach to augment radar data in a scaled environment that uses an off-the-shelf LiDAR as a high spatial resolution sensor.

Real-world Use of Automated Driving Systems and their Safety Consequences

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This study will leverage data collected from 50 participants who drove personally owned vehicles equipped with ADSs for 12 months. The work is expected to contribute to a greater understanding of the prevalence and safety consequences of ADS use on public roadways, as well as drivers’ perception of the early production ADS.

An Evaluation of Road User Interactions with E-Scooters

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This research project will investigate road user interactions with e-scooters.

Evaluation of Eyes Off Road During L2 Activation on Uncontrolled Access Roadways

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The goal of this research will be to evaluate the eye glance patterns of drivers operating L2 vehicles (ACC + lane centering) during normal, baseline driving while negotiating surface streets.

Guiding Driver Responses During Manual Takeovers from Automated Vehicles

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This project will leverage VTTI’s virtual reality driving platform that allows the research team to rapidly prototype various HMI options and examine human-subjects’ responses in various takeover scenarios.

Characterizing Level 2 Automation in a Naturalistic Driving Fleet

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For this Safe-D project, dash video from the NOVA fleet collection effort will be analyzed using machine vision to, combined with additional approaches that offer some redundancy, determine the frequency, timing, and characteristics of L2 feature activations and deactivations.

Impacts of Connected Vehicle Technology on Automated Vehicle Safety

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This project seeks to understand the existing systems and how they can be leveraged to provide the City with insight and suggested countermeasures to address the safety issues on these roadways. ​​​​​​​​​​​​

Data Mining Twitter to Improve Automated Vehicle Safety

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This project seeks to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis.

A Sensor Fusion and Localization System for Improving Vehicle Safety In Challenging Weather Conditions

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This project will use a combination of Radars and FIR cameras in addition to a LIDAR based system to map the environment and localize the vehicle with respect to the lanes on the road.

Development of a Connected Smart Vest for Improved Roadside Work Zone Safety

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This project will develop a wearable worker localization and communication device (i.e., Smart Vest) that utilizes the previously developed Threat Detection Algorithm (Safe-D project 03-050) to communicate workers’ locations to passing CAVs and proactively warn workers and passing motorists of potential collisions. ​​​​​

Safety Impact Evaluation of a Narrow Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway

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This project seeks to evaluate the safety impact of an innovative infrastructure solution for safe and efficient integration of Automated Vehicle (AV) as an emerging technology into an existing transportati​on system​. ​​​

Automated Truck Mounted Attenuator

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This project will develop an automated control system for TMA vehicles using a short following distance, leader-follower control concept which will remove the driver from the at-risk TMA vehicle.

Signal Awareness Applications

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This project seeks to enhance the current capabilities of VCC platforms by developing new signal awareness safety and mobility features. In addition, this project will investigate the technical and human factors constraints associated with user interfaces for notifying and alerting drivers to pertinent intersection-related information to curb unsafe driving behaviors at signalized intersections.

Development of a Diagnostic System for Air Brakes in Autonomous and Connected Trucks

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This project will develop a diagnostic system for estimating the leakage and stroke of the pushrod and corroborating the efficacy of the developed system on an experimental air brake system setup. ​​

Examining Senior Drivers’ Adaptation to Mixed-Level Automated Vehicles: A Naturalistic Approach – Phase II Analysis of the Naturalistic Driving Data

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This project will analyze these already-collected NDS data to evaluate the safety and mobility benefits of automated vehicle technologies AVT for senior drivers. ​​​​

Reference Machine Vision for ADAS Functions

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This project will develop a reference Lane Detection (LD) system that will provide a benchmark for evaluating different lane markings and perception algorithms. ​​​​​​​​​

Development of an Infrastructure Based Data Acquisition System (iDAS) to Naturalistically Collect the Roadway Environment

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This project seeks to understand the existing systems and how they can be leveraged to provide the City with insight and suggested countermeasures to address the safety issues on these roadways. ​​​​​​​​​​​​

Creating a Smart Connected Corridor to Support Research into Connected and Automated Vehicles

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This project will first define the needs and requirements for a CAV testbed​, plan, procure, and deploy the baseline equipment, and demonstrate testing capabilities for CAV safety applications.

Standardized Performance Evaluation of Vehicles with Automated Capabilities

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The project goal is to create an initial set of standardized tests to explore whether they enable the ongoing evaluation of automated driving features as they evolve over time.

Modeling Driver Responses During Automated Vehicle Failures

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This project will develop a model of human behavior during automation failures that may be integrated into current and future design processes for automated vehicles.

Legal Tools for Barriers to Accessing Data Sets in the Age of AV/CV Technologies

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This project will the data ownership and privacy implications of big data collection and processing.

Response of Autonomous Vehicles to Emergency Response Vehicles

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The objective of this project is to explore how an autonomous vehicle must safely respond to different classes of emergency vehicles using sound, vision and other onboard sensors.

Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs)

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This project aims at addressing this problem by delivering a specification for a wearable worker localization and communication system prototype that utilizes ultra wide-band (UWB) technologies to facilitate real-time threat detection and warning algorithms.

Autonomous Emergency Navigation to a Safe Roadside Location

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The proposed research is to enable the vehicle to navigate autonomously to stop out of the travel path of following vehicles.

Assessing Alternative Approaches for Conveying Automated Vehicle ‘Intentions’

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The project will focus on the development and evaluation of an augmented reality interface integrated into a dynamic HMI intended to increase situational awareness of the driving system and environment.

Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles

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This project will identify the factors that contribute to crashes in mixed traffic with automated and human-driven vehicles through data analysis, simulation, and field tests. Moreover, it will develop measures and guidelines to minimize the risk of such crashes.

Examining Senior Drivers Adaptation to Level 2-3 Automated Vehicles: A Naturalistic Study

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This project will examine seniors’ attitudes towards automated vehicle technology (AVT) prior to any substantive exposure or use, then again after having the opportunity to explore and use AVT in the real world.

Identification of Railroad Requirements for the Future Automated and Connected Vehicle (AV/CV) Environment

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This project will examine freight and passenger railroad operational and infrastructure needs can be best considered in the development of future AV/CV system architecture.

Creating a Roadmap for Safe-D Research Themes and Application Areas: Future Directions in Disruptive Technology and Safety

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This project will examine disruptive technologies that could address critical transportation safety challenges in future years.

Formalizing Human-Machine Communication in the Context of Autonomous Vehicles

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This project will incorporate formalized communications into decision making algorithms of an autonomous vehicle.

Pavement Perspective on AV Safety through Optimizing Lateral Positioning Pattern

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This project will evaluate channelized traffic from a pavements perspective and develop guidelines for reducing AV/CV hydroplaning potential through optimizing a lateral wheel positioning pattern and designing more rut resistant pavement surfaces.

Driver Training for Automated Vehicle Technology

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The goal of the current work is to develop training protocol guidelines that can be used by automated vehicle trainers to optimize overall system use and transportation safety. This will be accomplished by first developing a taxonomy of the knowledge and skills required to operate NHTSA L2 and L3 automated vehicles.

Countermeasures to Detect and Combat Inattention While Driving Partially Automated Systems

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This project will investigate and develop countermeasures for problems that can arise when human drivers are required to recognize a fault and assume manual control of a vehicle which is partially-automated.

Implications of Truck Platoons for Roadside and Vehicle Safety Hardware

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This project will identify and prioritize the critical MASH TL5 roadside safety device(s) for truck platooning impact assessment in order to understand the associated roadside and occupant risks and hazards.