It is expected that in 25 years, Americans who are 65 years or older will account for about 20% of the whole population. As smart cities are also expected to become a reality within the same timeframe, starting to address the needs and concerns of such a large group becomes an essential part of the design of a future smart city. Here we specifically address the mobility needs of the elderly and those with limited means of transportation. We consider multiple small vehicle options that might provide on-demand or scheduled means of door-to-door transportation. The NSF-EAGER project focuses on examining basic research aspects of sensing and tracking potential sources of vehicle pedestrian collisions in densely crowded situations and socially acceptable distance for collision avoidance. The project will be providing input to the OSU/Columbus Global City Teams Challenge activity SMOOTH (Smart Mobile Operation: OSU Transportation Hub) and related demonstrations and help develop a working system. The key innovative contributions of this EAGER project are: development of a unifying framework for sensing and tracking in mixed traffic situations, acceptable automated driving within pedestrian zones, and evasive road maneuvering to avoid colliding with conventional human driven vehicles.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service. The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios. This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.

Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. One such domain is automotive systems. In this domain, a proliferation of advanced sensor technology is being fueled by an expanding range of autonomous capabilities (blind spot warnings, automatic lane-keeping, etc.). The limit of this expansion is full autonomy, which has been demonstrated in various one-off prototypes, but at the expensive of significant hardware over-provisioning that is not tenable for a consumer product. To enable features approaching full autonomy in a commercial vehicle, software infrastructure will be required that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost. This project is directed at the development of such infrastructure. The desired infrastructure will be developed by focusing on a particularly compelling challenge problem: enabling cost-effective driver-assist and autonomous-control automotive features that utilize vision-based sensing through cameras. This problem will be studied by (i) examining numerous multicore-based hardware configurations at various fixed price points based on realistic automotive use cases, and by (ii) characterizing the range of vision-based workloads that can be feasibly supported using the software infrastructure to be developed. The research to be conducted will be a collaboration involving academic researchers at UNC and engineers at General Motors Research. The collaborative nature of this effort increases the likelihood that the results obtained will have real impact in the U.S. automotive industry. Additionally, this project is expected to produce new open-source software and tools, new course content, public outreach through participation in UNC's demo program, and lectures and seminars by the investigators at national and international forums.

Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world. If automated vehicles had this ability, they could drive more intelligently, and share information about road and environment conditions, events, and anomalies to improve situational awareness and safety for other automated vehicles as well as human drivers. That is the goal of this project, to achieve a synergy between computer vision, machine learning and cyber-physical systems that leads to a safer, cheaper and smarter transportation sector, and which has potential applications to other sectors including agriculture, food quality control and environment monitoring. To achieve this goal, this project brings together expertise in computer vision, sensing, embedded computing, machine learning, big data analytics and sensor networks to develop an integrated edge-cloud architecture for (1) "anytime scene understanding" to unify diverse scene understanding methods in computer vision, and (2) "cooperative scene understanding" that leverages vehicle-to-vehicle and vehicle-to-infrastructure protocols to coordinate with multiple systems, while (3) emphasizing how security and privacy should be managed at scale without impacting overall quality-of-service. This architecture can be used for autonomous driving and driver-assist systems, and can be embedded within infrastructure (digital signs, traffic lights) to avoid traffic congestion, reduce risk of pile-ups and improve situational awareness. Validation and transition of the research to practice are through integration within City of Pittsburgh public works department vehicles, Carnegie Mellon University NAVLAB autonomous vehicles, and across the smart road infrastructure corridor under development in Pittsburgh. The project also includes activities to foster development of a new cyber-physical systems workforce, though involvement of students in the research, co-taught multi-disciplinary courses, and co-organized workshops.