Safe-D: Safety through Disruption

Projects: Automated Vehicles


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.

Preparing Work Zones for Automated and Connected Vehicles

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This project will examine how transportation agencies, contractors, and other stakeholders can best plan, design, and implement work zones to accommodate and support connected and automated vehicle (CAV) operations.

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.