The objective of this research is to scale up the capabilities of fully autonomous vehicles so that they are capable of operating in mixed-traffic urban environments (e.g., in a city such as Columbus or even New York or Istanbul). Such environments are realistic large-city driving situations involving many other vehicles, mostly human-driven. Moreover, such a car will be in a world where it interacts with other cars, humans, other external effects, and internal and external software modules. This is a prototypical CPS with which we have considerable experience over many years, including participation in the recent DARPA Urban Challenge. Even in the latter case, though, operation to date has been restricted to relatively “clean” environments (such as multi-lane highways and simpler intersections with a few other vehicles). The approach is to integrate multidisciplinary advances in software, sensing and control, and modeling to address current weaknesses in autonomous vehicle design for this complex mixed-traffic urban environment. All work will be done within a defined design-and-verification cycle. Theoretical advances and new models will be evaluated both by large-scale simulations, and by implementation on laboratory robots and road-worthy vehicles driven in real-world situations. The research address significant improvements to current methods and tools to enable a number of formal methods to move from use in limited, controlled environments to use in more complex and realistic environments. The theory, tools, and design methods that are investigated have potential application for a broad class of cyber-physical systems consisting of mobile entities operating in a semi-structured environment. This research has the potential to lead to safer autonomous vehicles and to improve economic competitiveness, the nation's transportation infrastructure, and energy efficiency. The richness of the domain means that expected research contributions can apply not only to autonomous vehicles but, also, to a variety of related cyber-physical systems such as service robots in hospitals and rescue robots used after natural disasters. The experimental research laboratory for the project is used for undergraduate and graduate courses and supports new summer outreach projects for high-school students. Research outcomes are integrated with undergraduate and graduate courses on component-based software.

This project is developing techniques for secured real-time services for cyber-physical systems. In particular, the research is incorporating real-time traffic modeling techniques into the security service, consequently enhancing both system security and real-time capabilities in an adverse environment. While this proposed methodology has not yet been fully tested, it is potentially transformative. To defend against traffic analysis attacks, the research is developing algorithms that can effectively mask the actual operational modes of cyber-physical applications without compromising the guaranteed quality of service. This is achieved by using the traffic modeling theory, developed by the PIs, to precisely manage the network traffic at the right time and the right place. This traffic modeling theory can also help in develop efficient attack detection and suppression methods that can identify and restrain an attack in real-time. The proposed methods are expected to be more effective, efficient, and scale-able than traditional methods.

The objective of this research is to study, develop and implement a comprehensive set of techniques that will eventually enable automobiles to be driven autonomously. The approach taken is to (a) address cyber-physical challenges of reliable, safe and timely operations inside the automobile, (b) tackle a range of physical conditions and uncertainties in the external environment, (c) enable real-time communications to and from the automobile to other vehicles and the infrastructure, and (d) study verification and validation technologies to ensure correct implementations. Intellectual Merits: The project seeks to make basic research contributions in the domains of safety-critical real-time fault-tolerant distributed cyber-physical platforms, end-to-end resource management, cooperative vehicular networks, cyber-physical system modeling and analysis tools, dynamic object detection/recognition, hybrid systems verification, safe dynamic behaviors under constantly changing operating conditions, and real-time perception and planning algorithms. Multiple intermediate capabilities in the form of active safety features will also be enabled. Broader Impacts: Automotive accidents result in about 40,000 fatalities and 3 million injuries every year in the USA. The global annual cost of road injuries is $518 billion. Many accidents are due to humans being distracted. Autonomous vehicles controlled by ever-vigilant cyber-physical systems can lead to significant declines in accidents, deaths and injuries. Autonomous vehicles can also offload driving chores from humans, and make time spent in automobiles more productive. Vehicular networks can help find the best possible routes to a destination in real-time. Broader impacts in this area are amplified by the project's partnerships with companies in the transportation and agricultural technology industries, and in information technology. Broader impacts are also sought through demonstrations and outreach to attract students into science and technology, and in particular to cyber-physical systems research.

This project has two closely related objectives. The first is to design and evaluate new Cyber Transportation Systems (CTS) applications for improved traffic safety and traffic operations. The second is to design and develop an integrated traffic-driving-networking simulator. The project takes a multi-disciplinary approach that combines cyber technologies, transportation engineering and human factors. While transportation serves indispensible functions to society, it does have its own negative impacts in terms of accidents, congestion, pollution, and energy consumption. To improve traffic safety, the project will develop and evaluate novel algorithms and protocols for prioritization, delivery and fusion of various warning messages so as to reduce drivers? response time and workload, prevent conflicting warnings, and minimize false alarms. To improve traffic operations, the project will focus on the design of next generation traffic management and control algorithms for both normal and emergency operations (e.g. during inclement weather and evacuation scenarios). Both human performance modeling methods and human subjects? experimental methods will be used to address the human element in this research. As the design and evaluation of CTS applications requires an effective development and testing platform linking the human, transportation and cyber elements, the project will also design and develop a simulator that combines the main features of a traffic simulator, a networking simulator and a driving simulator. The integrated simulator will allow a human driver to control a subject vehicle in a virtual environment with realistic background traffic, which is capable of communicating with the driver and other vehicles with CTS messages. Background traffic will be controlled by a realistic driver model based on our human factors research that accounts for CTS messages? impact on driver behavior. Intellectual Merits: The project explicitly considers human factors in the design and evaluation of CTS safety and operations applications, a topic which has not received adequate attention. Moreover, the proposed integrated simulator represents a first-of-a-kind simulator with unique features that can reduce the design and evaluation costs of new CTS applications. Broader Impacts: The proposed research can improve the safety, efficiency and environmental-friendless of transportation systems, which serve as the very foundation of modern societies and directly affects the quality of life. The integrated simulator will be used as a tool for teenage and elderly driver education and training, and to inspire minority, middle and high school students to pursue careers in math, science, and computer-related fields

Vehicle automation has progressed from systems that monitor the operation of a vehicle, such as antilock brakes and cruise control, to systems that sense adjacent vehicles, such as emergency braking and intelligent cruise control. The next generation of systems will share sensor readings and collaborate to control braking operations by looking several cars ahead or by creating safe gaps for merging vehicles. Before we allow collaborative systems on public highways we must prove that they will do no harm, even when multiple rare events occur. The events will include loss of communications, failures or inaccuracies of sensors, mechanical failures in the automobile, aggressive drivers who are not participating in the system, and unusual obstacles or events on the roadways. The rules that control the interaction between vehicles is a protocol. There is a large body of work to verify the correctness of communications protocols and test that different implementations of the protocol will interact properly. However, it is difficult to apply these techniques to the protocols for collaborative driving systems because they are much more complex: 1) They interact with the physical world in more ways, through a network of sensors and the physical operation of the automobile as well as the communications channel; 2) They perform time critical operations that use multiple timers; And, 3) they may have more parties participating. In [1] we have verified that a three party protocol that assists a driver who wants to merge between two cars in an adjacent lane will not cause an accident for combinations of rare events. The verification uses a probabilistic sequence testing technique [2] that was developed for communications protocols. We were only able to use the communications technique after designing and specifying the collaborative driving protocol in a particular way. We have generalized the techniques used in the earlier work so that we can design collaborative driving protocols that can be verified. We have 1) a non-layered architecture, 2) a new class of protocols based upon time synchronized participants, and 3) a data management rule. 1) Communications protocols use a layered architecture. Protocol complexity is reduced by using the services provided by a lower layer. The layered architecture is not sufficient for collaborative driving protocols because they operate over multiple physical platforms. Instead, we define a smoke stack architecture that is interconnected. 2) The operation of protocols with multiple timers is more difficult to analyze because there are different sequences of operations depending on the relative times when the timers are initiated. Instead of using timers, we design protocols that use absolute time. This is reasonable because of the accurate time acquired from GPS and the accuracy of current clocks while GPS is not available. 3) Finally, in order for programs in different vehicles to make the same decisions they must use the same data. Our design merges the readings of sensors in different vehicles and uses a communications protocol that guarantees that all vehicles have the same sequence of messages and only use the messages that all vehicles have acquired. 1. Bohyun Kim, N. F. Maxemchuk, "A Safe Driver Assisted Merge Protocol," IEEE Systems Conference 2012, 19-22 Mar. 2012, Vancouver, BC, Canada, pp. 1-4. 2. N. F. Maxemchuk, K. K. Sabnani, "Probabilistic Verification of Communication Protocols," Distributed Computing Journal, Springer Verlag, no. 3, Sept. 1989, pp. 118-129.

The objective of this research is to develop technologies to improve the efficiency and safety of the road transportation infrastructure. The approach is to develop location-based vehicular services combining on-board automotive computers, in-car devices, mobile phones, and roadside monitoring/surveillance systems. The resulting vehicular Cyber Physical Systems (CPS) can reduce travel times with smart routing, save fuel and reduce carbon emissions by determining greener routes and commute times, improve safety by detecting road hazards, change driving behavior using smart tolling, and enable measurement-based insurance plans that incentivize good driving. This research develops distributed algorithms for predictive travel delay modeling, feedback-based routing, and road hazard assessment. It develops privacy-preserving protocols for capturing and analyzing data and using it for tasks such as congestion-aware tolling. It also develops a secure macro-tasking software run-time substrate to ensure that algorithms can be programmed centrally without explicitly programming each node separately, while ensuring that it is safe to run third-party code. The research focuses on re-usable methods that can benefit multiple vehicular services, and investigates which lessons learned from this vehicular CPS effort generalize to other situations. Road transportation is a grand challenge problem for modern society, which this research can help overcome. Automobile vendors, component developers, and municipal authorities have all shown interest in deployment. The education plan includes outreach to local K-12 students and a new undergraduate course on transportation from a CPS perspective, which will involve term projects using the data collected in the project