In March 2020, C2M2 sent out a Call for Proposals to researchers at our five partner institutions, launching our 2020/2021 round of funded projects. This call for proposals was also announced on our website, and Twitter feed, inviting affiliated researchers to apply for funding. As with previous years, multi-institution collaboration was prioritized and in this round focus was placed on big picture projects. Multiple new PIs received funding to increasing the breadth of research topics funded by our Center. 15 research proposals were submitted to C2M2 for potential funding, with five new principal investigators (PIs) submitting, and nine returning PIs, covering a myriad of new topics. As part of our selection process proposals were then sent out for blind review by industry professionals from academia, and public and private agencies. Each proposal received at least three reviews, which were then used to select projects for funding. C2M2 Directors, Drs. Chowdhury (Clemson University), Huynh (University of South Carolina), Comert (Benedict College), Mwakalonge (South Carolina State University), and Michalaka (the Citadel) met virtually this year to evaluate research proposals for our 2020/2021 round of funded research. At this time, eight research projects out of the 15 submitted proposals were selected for funding based on their reviews and began in early September of 2020. Of these eight selected projects, three are led by Clemson University, one is led by SCSU, three are led by USC, and Benedict College will lead one of the eight selected projects. The Citadel will collaborate on one of the selected projects.
Cloud-based Collaborative Road Condition Monitoring using In-Vehicle Smartphone Data and Deep Learning
Lead Principal Investigator – Yunyi Jia, Clemson University
Co-Principal Investigator(s) – Gurcan Comert, Benedict College
September 2020 – July 2023
Project Status – Ended
Funding Amount – UTC $67,215, Clemson $25,445, Benedict College $9,609
Total Funding – $102,269
Sponsoring Orgs – OST-R, Clemson University, Benedict College
Description: Ensuring safety of transportation systems, especially multimodal connected and autonomous transportation systems, requires to monitor the conditions of roads. Traditional monitoring and inspection of road condition require surveyors to walk or drive along the roads to search for defects manually. Such processes require a lot of human and equipment efforts, which however can still hardly provide timely needed information of road conditions. Existing automated road condition monitoring approaches usually require special vehicles equipped with specific seniors and corresponding processing and computing devices, which increases the cost of the approaches. In addition, these existing approaches only use one single vehicle to perform the detection by its own and the vehicle usually still needs to be driven by a surveyor, which still requires a large amount of efforts to monitor the roads in terms of labor and equipment costs. Therefore, a more cost-effective and efficient road condition monitoring approach is needed.
Intellectual Merit: The research goal is to develop a cost-effective approach to monitor the road conditions by cloud-based collaborative monitoring using in-vehicle smartphones which could be from any general public vehicle users.
Broader Impacts: This project aims to reduce the cost of road condition monitoring by providing a very cost-effective way with minimum investment of equipment and labors, significantly improve the safety of transportation systems, especially the multimodal connected and automated transportation systems, by providing timely needed road condition monitoring, and create a smartphone-based road condition dataset to benefit the research society.
Potential Reduction of Fatal Crashes in South Carolina due to Connected and Automated Vehicles
Lead Principal Investigator – Wayne Sarasua, Clemson University
Co-Principal Investigator(s) – Dimitra Michalaka, The Citadel; Pamela Murray-Tuite, Clemson University; Kweku Brown, The Citadel; Jennifer Ogle, Clemson University; William J. Davis, The Citadel
September 2020 – November 2023
Project Status – Ended
Funding Amount – UTC $105,442, Clemson $23,783, The Citadel $28,938
Total Funding – $158,163
Sponsoring Orgs – OST-R, Clemson University, The Citadel
Description: Traffic fatalities consistently rank South Carolina among the highest rates for fatalities per VMT and fatalities per 100,000 population in the United States. Furthermore, South Carolina incurs over 4.5 billion dollars in economic loss annually due to roadway traffic crashes (SCDPS, 2018). In 2018, there were 158,777 reported motor vehicle crashes in South Carolina resulting in 1,034 fatalities and 58,044 injuries. Smart driving technologies have the potential to change the safety paradigm with regard to traffic fatalities. In this research, the investigators propose to study the potential safety impacts of connected and automated vehicles (CAVs) on fatal crash occurrence in South Carolina. We propose to study how different levels of autonomy and different levels of market penetration of CAVs can reduce certain types of fatal crashes.
Intellectual Merit: There has been a tremendous amount of safety research that has been done over the years. While research on safety continues to be at the forefront of transportation research dollars, fatal crash frequency and fatal crash rates are on an upward trend. A new paradigm is needed to really impact safety and make a vision of 0 roadway fatalities a reality. The greatest contributor to fatal crashes (and all crashes for that matter) is by far the driver. Over 90% of crashes and of fatal crashes can be attributed in whole or in part to the driver. Taking the driver out of the equation is the only way to really eliminate or at least dramatically reduce fatal crash incidence in South Carolina (and the US). Definitive research to quantify the reduction in fatal crashes can help to proliferate CAV technologies in the future.
Broader Impacts: The overall research goal is to help state departments of transportation (DOTs) dramatically reduce the number and rate of fatal crashes. This will in-turn improve overall safety of our nation’s roads. Other objectives of the proposed research include:
• Identifying how safety-related driving parameters for CAVs differ across varying levels of autonomy
• Examining how these parameters lead to different crash outcomes (e.g., no crash, non-fatal crash) compared to known fatal crashes from the last three years in SC
• Identifying CAV capabilities most effective in reducing fatal crashes
• Identifying future research initiatives related to this initial research work.
• Helping DOTs to establish priorities for infrastructure investments and policies for implementation of CAV capabilities
• Influencing automakers to incorporate those CAV capabilities in automobiles that would have the greatest impact on fatal crash rates.
Safe and Efficient E-Wayfinding (SeeWay) Guidance for the Transition to Autonomous Vehicles for the Visually Impaired
Lead Principal Investigator – Bing Li, Clemson University
Co-Principal Investigator(s) – Gurcan Comert, Benedict College; Johnell Brooks, Clemson University; Aries Arditi, Visibility Metrics
September 2020 – September 2021
Project Status – Ended
Funding Amount – UTC $62,900, Clemson $21,000, Benedict College $7,450, Visibility Metrics $3,000
Total Funding – $94,350
Sponsoring Orgs – OST-R, Clemson University, Benedict College, Visibility Metrics
Description: Independent travel for blind and visually impaired (BVI) individuals is essential for maximizing quality of life, by enabling travel to work and recreational activities. Leveraging autonomous vehicles, efficient and safe indoor-to outdoor navigational guidance has the potential to fill this gap for BVI travelers. We will start our project by studying and exploring the needs, wants, and concerns of BVI travelers between indoor environments (including home, school, work, etc.) and the locations where they will access future autonomous vehicles in each of those locations to allow for independent travel. We will derive design criteria based upon a human factors analysis and the resulting models will guide our assistive navigation system development. Based on crowdsourcing and human-centered AI frameworks, we will explore the state-of-the-art computer vision and AI-based environment recognition technologies to enable efficient navigational guidance and improve travel safety for BVI individuals.
Intellectual Merit: The goal of our research is to enable Safe, Efficient and Electronic (E-) Wayfinding (SeeWay) guidance solution for the transition to facilitate BVI individuals to access autonomous vehicles by exploring and developing a portable and affordable solution.
Broader Impacts: Despite its challenges, independent travel for blind and visually impaired (BVI) individuals is an essential component of quality of life, enabling travel to work and recreational activities. Autonomous vehicle technologies have the potential of meeting these challenges. However, efficiently and safely guiding BVI travelers between indoor environments and vehicles outdoors remains a key obstacle. In the future transportation chain, assistive navigation technologies, connecting BVI travelers and vehicles, will be of extraordinary importance for BVI individuals in the context of social justice and health care/public health.
Digital Twins to Increase Mobility in Rural South Carolina
Lead Principal Investigator – Paul Ziehl, University of South Carolina
Co-Principal Investigator(s) – Gurcan Comert, Benedict College; Vafa Soltangharaei, University of South Carolina; Mahmoud Bayat, University of South Carolina
September 2020 – September 2021
Project Status – Ended
Funding Amount – UTC $78,846, University of South Carolina $25,391, Benedict College $14,425
Total Funding – $118,662
Sponsoring Orgs – OST-R, University of South Carolina, Benedict College
Description: The overarching research goal is to alter the way in which transportation infrastructure is evaluated. This will be approached through the leveraging of emerging technologies in the vehicular transportation sector, namely autonomy and vehicle to infrastructure (V2I) communications. In short, we foresee a future where bridge evaluation is carried out autonomously without the need for complex and costly loading scenarios. The proposed effort is a small and needed step toward that future. The specific objective of this project is to develop a ‘digital twin’ of for a bridge type that is commonly load restricted and serves much of rural South Carolina (Figure 2). This objective will be achieved through close collaboration with the SCDOT and an industrial collaborator, Structural Monitoring Solutions (www.smsshm.com). The project team will instrument a bridge and develop the digital twin by leveraging experience gained through similar projects in related fields, including monitoring and remote assessment of nuclear infrastructure and autonomous aircraft.
Intellectual Merit: The SCDOT is in the process of load rating all bridges in its very substantial inventory. We propose a very different approach grounded in our experience with live load testing in the field, full scale laboratory testing of bridge components, sensing with a focus on machine learning and edge analytics, and numerical simulation.
Broader Impacts: This project seeks to improve the mobility of freight as well as emergency vehicles and school buses in rural South Carolina.
• Recent advances in sensing, artificial intelligence, and communications will be leveraged to achieve this specific goal.
• The project team seeks to influence the development of Vehicle to Infrastructure (V2I) technology to greatly reduce the cost associated with bridge evaluation while increasing the fidelity of the evaluation.
• The project team is partnered with the SCDOT (through ongoing research investigations) and an industrial collaborator, Structural Monitoring Solutions, to assure the technology will be impactful and transferred.
Strategic Management of Limited Transportation Recourses to Support Mobility of Disadvantaged and Disabled Travelers during the COVID-19 Pandemic or Similar Situations
Lead Principal Investigator – Yu Qian, University of South Carolina
Co-Principal Investigator(s) – Gurcan Comert, Benedict College; Negash Begashaw, Benedict College
September 2020 – September 2021
Project Status – Ended
Funding Amount – UTC $69,215, Clemson $25,057, Benedict College $9,609
Total Funding – $103,881
Sponsoring Orgs – OST-R, University of South Carolina, Benedict College
Description: The COVID-19 lock down has reduced public transportation service to the disadvantaged and disabled people who are in urgent need of adequate mobility to obtain essential suppliers. The aim of this proposal is to improve the life quality of people with disabilities and elderly people by addressing issues related to social exclusion, accessibility, and mobility. Demand responsive transport services are frequently offered in the context of door-to-door transportation of the elderly and persons with disabilities. We propose to study an optimization methodology to analyze the minimal resource requirements of a demand responsive system in terms of vehicles, drivers required, total distance traveled, by means of a vehicle routing plan, taking into account heterogeneous users (persons with different severity and type of disabilities), heterogeneous vehicles (regular and wheelchair adapted vehicles) and multiple geographically distributed depots (locations where vehicles are stored). The general strategic planning problem of allocating and relocating resources in service zones will be formulated and solved. The proposed model could be implemented with given geographic conditions and other local information to be tailored for specific applications for local communities. It is anticipated that the outcome from this research would benefit disadvantaged and disabled travelers during COVID-19 or similar difficult situations in the future.
Intellectual Merit: • Benefit disadvantaged and disabled travelers during COVID-19 or similar pandemic lock down
• Maximize travel request with limited resources available at local communities
• Strategic planning problem of allocating and relocating resources in service zones
• Develop a generic methodology that can be implemented with given geographic scenarios
• Consider both route optimization and ride request uncertainty
Broader Impacts: The proposed project aims to boost the positive social impact by fulfilling uncertain travel requests from the disadvantaged and disabled people, while minimizing the utilization of assets and resources, such as fleet size, human resources, and funding. The project will formulate and solve a general strategic planning problem of allocating and relocating fleets in service zones. Though the proposed model is general and stylized, it can be easily tailored and implemented with different geographic conditions and other local information for specific applications for local communities. It is anticipated that the outcome from this research would serve and benefit the disadvantaged and disabled travelers during COVID-19 or similar difficult situations in the future.
Improving Freight Transport Mobility and Efficiency via Synchronization
Lead Principal Investigator – Nathan Huynh, University of South Carolina
Co-Principal Investigator(s) – William Ferrell, Clemson University, Scott Mason, Clemson University
September 2020 – Active
Project Status – Report in Progress
Funding Amount – UTC $67,999, University of South Carolina $27,775, Clemson $15,828
Total Funding – $112,602
Sponsoring Orgs – OST-R, University of South Carolina, Clemson University
Description: This research project will explore opportunities for dynamic re-planning of a facility’s inbound and outbound shipments as well as the rescheduling of trucks to minimize the impact of disruptions on the supply chain. A proof of concept system, focusing on cross-docks, will be developed. The goal of the proof-of-concept is to demonstrate how current connected vehicles and V2I technologies can be used to enable two-way communications between the cross-dock and connected trucks at different distances/time intervals to enable synchronized operations. To accomplish this, cross-dock operations and truck movements will be simulated and evaluated in an emulated environment using the Connected Vehicle Application Development Platform (CVDeP) developed by C2M2.
Intellectual Merit: The goals and objectives of the proposed research are to explore opportunities for dynamic re-planning of a facility’s inbound and outbound shipments as well as the rescheduling of trucks to minimize the impact of disruptions on the supply chain. A proof of concept system will be developed to show that dynamic re-planning and rescheduling can be facilitated by using connected vehicles and V2I. It is anticipated that the proposed proof-of-concept can be extended and applied to actual freight systems. Successful implementation of freight synchronization will create a transportation system that is: 1) more resilient to disruptions and 2) more efficient by requiring fewer resources to produce higher throughput. Additional benefits include reduced cost, fuel/energy consumption, and carbon footprint.
Broader Impacts: The broad aim of this research project is to produce a useful and practical tool. To this end, the developed models and tools will be made available for use by South Carolina’s DOT, Council of Governments, and Department of Commerce. Codes, sample data sets, and tutorials will be made available on the C2M2 website as well as on Github for other researchers to access, analyze, and extend.
Modeling Impact of Weather Conditions on 5G Communication and Mitigation Measures on Control of Automated Intersections
Lead Principal Investigator – Gurcan Comert, Benedict College
Co-Principal Investigator(s) – Pierluigi Pisu, Clemson University; Esmail Abuhdima, Benedict College; Chin Tser Huang, University of South Carolina
September 2020 – May 2022
Project Status – Ended
Funding Amount – UTC $123,121, Benedict College $9,609, University of South Carolina $24,898, Clemson University $24,000
Total Funding – $186,188
Sponsoring Orgs – OST-R, Benedict College, University of South Carolina, Clemson University
Description: In this study, we propose to model the impact of weather on sensors and communication under 5G framework and develop a robust intersection control. Studies showed that 5G will be capable of meeting latency standards for connected and autonomous vehicles (CAVs) applications (Khan et al. 20201). Currently, CAVs intersection control deals with perfect communications and focus on control algorithms. We aim to address the communication layer with the impact of weather. Based on the degradation/statistical models, optimum control parameters will be revised. Existing intersection control uses scheduling, polling, and reservation type control. Models are showing promise of the automated intersections, but do not break them to test vulnerability from various uncertainty sources such as weather.
Intellectual Merit: This project aims to address the effect of weather factors on intelligent transportation systems applications under connected and autonomous vehicles (CAVs) 5G (mm-Wave) communications and sensors. The study will look at safety critical application of autonomous intersection control with connectivity, mathematical communication models will be produced that address the impact of weather factors on packet drops and safe communication ranges, and dynamic control algorithms for ITS applications will be developed incorporating the modeled uncertainty due to climatic factors.
Broader Impacts: This project aims to produce the following impacts:
• Communication models for 5G (mm-Wave radar) with the impact of a variety of weather factors will be developed that can be used in any simulation.
• Robust autonomous intersection control in presence of weather factor related communication uncertainties will be developed.
Monitoring of Illegal Removal of Road Barricades using Intelligent Transportation Systems in Connected and Non-Connected Environments
Lead Principal Investigator – Judith Mwakalonge, South Carolina State University
Co-Principal Investigator(s) – Saidi Siuhi, South Carolina State University; Gurcan Comert, Benedict College
September 2020 – September 2021
Project Status – Ended
Funding Amount – UTC $37,944, South Carolina State University $10,713, Benedict College $7,000
Total Funding – $55,657
Sponsoring Orgs – OST-R, South Carolina State University, Benedict College
Description: Emergency officials usually use physical barricades to close a road/lane(s) during emergencies. The barricades commonly used include concrete barriers, metal, cones, etc. The barricades, however, can be illegally removed by a perpetrator(s) with intentions to harm road users. These incidences where the barricades were illegally removed resulted in fatalities, injuries, and property damages. In many incidences reported, the emergency officials were unable to determine the time when barricades were removed from the location. This shows that non-smart barricades do not include redundancy in the design that would alert officials and warn road users of a road/lane(s) closure to prevent an impending danger even when the barricade is removed illegally by a perpetrator(s). This research intends to propose smart road/lane monitoring and warning systems that would warn road users and alert emergency officials of the impending danger even when the physical barricades have been removed illegally while the road/lane(s) closure is still effective. This smart monitoring and warning system will consider both connected and non-connected vehicles. Because of the increasing trend in the occurrences of climate-related events, there is a demand for this type of technology to save life and damage losses.
Intellectual Merit: This project will conduct a comprehensive review of the literature on practices commonly used for road/lane(s) closure and summarize the key findings to identify the problems of the current non-smart barricades used for road/lane(s). Researchers will then propose smart road/lane closure monitoring and warning systems that support connected and non-connected vehicles to improve safety, and recommend the best road/lane(s) closure monitoring and warning systems to support connected and non-connected vehicles.
Broader Impacts: The proposed smart road/lane(s) closure monitoring and warning systems are expected to significantly reduce traffic accidents resulting from illegal removal of road/lane(s) closure barricades. The smart system will provide a secondary system that can warn motorists of the impending hazard even when the physical barriers were removed intentionally before an accident occurs. Additionally, the smart system will alert responsible emergency agencies that the barrier has been illegally removed so that they take immediate action to protect all road users before an accident occurs.