The Safe-D Fall 2018 Workshop was held on September 12, 2018, kicking off the Fall 2018 Call for Proposals.
At the Workshop, Targeted Topics from the Safe-D Stakeholder Advisory Board were presented to researchers interested in proposing projects under this competition. Awards from this competitive cycle were expected to be made during December 2018, however with the delayed release of Year 3 funding from USDOT, final awards were deferred until 2019.
Under the Fall 2018 Competition, the Safe-D Leadership Team received 36 proposals (research statements), including 11 which were collaborative across consortium university team members. Of these, 11 projects were selected to give a 7-minute entrepreneurial style pitch presentation to Safe-D Leadership Team and Safe-D Stakeholder Advisory Board as part of the new Safe-D T2 Plan process. The Invited PI Pitch Session allowed researchers to present the value of the project to industry partners and allow for direct questions and answers that could help refine the project. Ultimately, 12 projects were awarded this competition cycle.
Beyond competitive research projects, consortium members VTTI and TTI also funded three directed projects, including one project focused on crowdsourced data (TTI) and two projects working with DOT partners to develop advanced transportation technologies (VTTI). These directed projects include leveraged external funding from the State of Texas and VDOT.
The following are descriptions of these newly awarded projects:
Project 04-098: Data Mining Twitter to Improve Automated Vehicle Safety
Institution(s): TTI*, VT; Award Round: Fall 2018; Theme Area(s): Big Data Analytics, Automated Vehicles
Automated vehicle technologies may significantly improve driving safety, but only if they are widely adopted and if drivers use them appropriately. Prior work suggests that intentions to adopt new technology and appropriately rely on it are often driven by the user’s expectations. This project will seek to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis. We further focus on responses and changes of opinion surrounding automated vehicle crashes.
Project 04-100: Development of a Diagnostic System for Air Brakes in Autonomous and Connected Trucks
Institution(s): TTI*; Award Round: Fall 2018; Theme Area(s): Automated Vehicles, Connected Vehicles, Transportation as a Service
Air brakes are sensitive to maintenance and are employed in trucks; proper functioning of air brakes is critical to safety of autonomous and connected trucks. This project is concerned with developing 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 that will be built as a part of this project. The proposed diagnostic system can be used to facilitate pre-trip and enforcement inspections of air brakes in trucks and as an on-board monitoring system for air brakes in trucks in Autonomous and Connected Trucks.
Project 04-101: Safety Impact Evaluation of a Narrow Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway
Institution(s): SDSU*; Award Round: Fall 2018; Theme Area(s): Automated Vehicles
The main objective of this research is 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 transportation system. Filling the gap in the limited research on the effect of AV technology on infrastructure standards, this project will evaluate whether AVs could operate safely in a narrow lane next to regular traffic lanes on an expressway.
Project 04-103: Examining Senior Drivers’ Adaptation to Mixed-Level Automated Vehicles: Phase II
Institution(s): VTTI*; Award Round: Fall 2018; Theme Area(s): Automated Vehicles
This study is envisioned as a follow-on to a recently-completed 2017 Safe-D project (Phase I). The first phase was focused on how driving exposure to automated vehicle technologies (AVT) can change how seniors, a particularly vulnerable and growing segment of our population, use and accept AVT over time. The goal of the Phase II follow-on proposed herein is to analyze these already-collected NDS data to evaluate the safety and mobility benefits of AVT for senior drivers.
Project 04-104: Development of a Connected Smart Vest for Improved Roadside Work Zone Safety
Institution(s): VTTI*, TTI; Award Round: Fall 2018; Theme Area(s): Connected Vehicles, Automated Vehicles
Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in work zones in the United States due to motor vehicle traffic crashes. This project aims to 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.
Project 04-110: Developing an Intelligent Transportation Management Center (ITMC) with a Safety Evaluation Focus for Smart Cities
Institution(s): SDSU*, VTTI; Award Round: Fall 2018; Theme Area(s): Big Data Analytics
Traditional transportation management centers (TMCs) have limited capability to utilize large amounts of data to properly evaluate transportation safety. The goal of this project is to develop an intelligent transportation management center (ITMC) that adopts automated video data analysis to evaluate safety. The proposed ITMC demonstrates how intelligent transportation systems (ITS) technologies and big data analytics can be utilized to proactively assess transportation safety at signalized intersections.
Project 04-113: Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements
Institution(s): TTI*, VTTI; Award Round: Fall 2018; Theme Area(s): Big Data Analytics
The goal of this project is to examine whether traffic volume estimates developed from disruptive technologies such as cell phones, GPS/Bluetooth devices, and alternative data sources (e.g., demographic, socioeconomic, land use data) can be used confidently and accurately to support data-driven safety analysis (i.e., network screening) to meet the 2016 Highway Safety Improvement Program (HSIP) Final Rule requirements.
Project 04-114: Behavior-based Predictive Safety Analytics Phase II
Institution(s): VTTI*, SDSU, TTI; Award Round: Fall 2018; Theme Area(s): Big Data Analytics
This project addresses the emerging field of behavior-based predictive safety analytics, focusing on the prediction of road crash involvement based on individual driver behavior characteristics. This project continues work from a pilot study that created a proof-of-concept demonstration of how crash involvement may be predicted on the basis of individual driver behavior utilizing SHRP2 naturalistic data. This project seeks to analyze large scale continuous naturalistic data as well as event data, both public and proprietary, to study the role of different driving behaviors in the buildup of a safety critical event.
Project 04-115: Reference Machine Vision for ADAS Functions
Institution(s): TTI*; Award Round: Fall 2018; Theme Area(s): Automated Vehicles, Connected Vehicles
Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning (LDW) and Lane Keep Assist (LKA) systems are used effectively. However, these systems are not yet popular because the systems are not robust due, in part to the lack of suitable standards for pavement markings that enable reliable functionality of the sensor system. The objective of this project is to develop a reference Lane Detection (LD) system that will provide a benchmark for evaluating different lane markings and perception algorithms.
Project 04-117: A Sensor Fusion and Localization System for Improving Vehicle Safety In Challenging Weather Conditions
Institution(s): TAMU*; Award Round: Fall 2018; Theme Area(s): Automated Vehicles, Connected Vehicles, Transportation as a Service
The safety of autonomous\connected vehicles primarily relies on their ability to accurately sense the environment. The sensing problem is significantly challenging in weather conditions which include sudden change in lighting, smoke, fog, snow, and rain. The objective of this project is to 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. This project will develop a prototype of an all weather sensing and localization system which will be useful for any autonomous or connected vehicle. The performance of the developed system will be corroborated with several data sets collected at Rellis.
Project 04-120: Impacts of Connected Vehicle Technology on Automated Vehicle Safety
Institution(s): VTTI*; Award Round: Fall 2018; Theme Area(s): Connected Vehicles, Automated Vehicles
Data shared over connected vehicle technologies (CVT) may provide a variety of performance benefits to transportation. CVT could improve safety as events unfold; however, although previous work has characterized some of the potential advantages of connectivity on human operated vehicles, the impacts of connectivity on automated driving systems (ADS) is not well established. The purpose of this proposed effort is to conduct a focused effort which leverages the SHRP2 Naturalistic Driving Study to estimate the potential impact of connectivity on safety for future on automated driving systems (ADS) in transportation.
Project 04-121: Development of an Infrastructure Based Data Acquisition System (iDAS) to Naturalistically Collect the Roadway Environment
Institution(s): VTTI*; Award Round: Fall 2018; Theme Area(s): Big Data Analytics, Connected Vehicles, Automated Vehicles
Roadways in the City of Virginia Beach have consistently ranked in the top 10 crash cluster locations in the state of Virginia, many of which occur at signal controlled intersections that control flow on to and off of the only major interstate in the City, I-264. Further, the Virginia Department of Transportation’s (VDOT) Pedestrian Action Plan has identified Pacific Avenue, a major oceanfront resort corridor, as a priority pedestrian crash cluster location. 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. In addition, this project seeks to develop methods to populate data from such sensors into formats that can be utilized by the industry to assist in the development of connected and automated vehicle safety systems.
Project TTI-04-01: Exploring Crowdsourced Monitoring Data for Safety
Institution(s): TTI*; Award Round: Directed 2018/2019 Projects; Theme Area(s): Big Data Analytics
This project includes three separate, but related exploratory studies working with emerging data sources, to potentially improve roadway safety analysis. These projects will answer the following research questions: 1) Can bicyclist trips sensed passively through mobile location data support bicyclist activity indices at the city scale?, 2) Is it feasible to use crowdsourced incident data to provide a reliable and timely indicator of real-time crash risks?, and 3) How can exploratory data analysis (EDA) of hazard warning data inform usability for crash surrogate measures
Project VTTI-00-021: Signal Awareness Applications
Institution(s): VTTI*; Award Round: Directed 2018/2019 Projects; Theme Area(s): Connected Vehicles, Automated Vehicles
Intersection collisions account for 40% of all crashes on our nation’s roadways. It is estimated that 165,000 accidents, resulting in approximately 800 fatalities annually, are due to vehicles that pass-through intersections during red signal phases. Although infrastructure-based red-light violation countermeasures have been deployed, intersections remain a top location for vehicle crashes. This project proposes 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.
Project VTTI-00-022: Automated Truck Mounted Attenuator
Institution(s): VTTI*; Award Round: Directed 2018/2019 Projects; Theme Area(s): Automated Vehicles, Connected Vehicles
Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. While the TMA is designed to absorb and/or redirect the energy from a colliding vehicle, there is still significant risk of injury to the TMA driver when struck. This project seeks to 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.