A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture

Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical stochastic hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.Comment: This work has been accepted in 2018 IEEE Connected and Automated Vehicles Symposium (CAVS 2018

Planning for Density in a Driverless World

Automobile-centered, low-density development was the defining feature of population growth in the United States for decades. This development pattern displaced wildlife, destroyed habitat, and contributed to a national loss of biodiversity. It also meant, eventually, that commutes and air quality worsened, a sense of local character was lost in many places, and the negative consequences of sprawl impacted an increasing percentage of the population. Those impacts led to something of a shift in the national attitude toward sprawl. More people than ever are fluent in concepts of “smart growth,” “new urbanism,” and “green building,” and with these tools and others, municipalities across the country are working to redevelop a central core, rethink failing transit systems, and promote pockets of density. Changing technology may disrupt this trend. Self-driving vehicles are expected to be widespread within the next several decades. Those vehicles will likely reduce congestion, air pollution, and deaths, and free up huge amounts of productive time in the car. These benefits may also eliminate much of the conventional motivation and rationale behind sprawl reduction. As the time-cost of driving falls, driverless cars have the potential to incentivize human development of land that, by virtue of its distance from settled metropolitan areas, had been previously untouched. From the broader ecological perspective, each human surge into undeveloped land results in habitat destruction and fragmentation, and additional loss of biological diversity. New automobile technology may therefore usher in better air quality, increased safety, and a significant threat to ecosystem health. Our urban and suburban environments have been molded for centuries to the needs of various forms of transportation. The same result appears likely to occur in response to autonomous vehicles, if proactive steps are not taken to address their likely impacts. Currently, little planning is being done to prepare for driverless technology. Actors at multiple levels, however, have tools at their disposal to help ensure that new technology does not come at the expense of the nation’s remaining natural habitats. This Article advocates for a shift in paradigm from policies that are merely anti-car to those that are pro-density, and provides suggestions for both cities and suburban areas for how harness the positive aspects of driverless cars while trying to stem the negative. Planning for density regardless of technology will help to ensure that, for the world of the future, there is actually a world

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations

Transformational Autonomy and Personal Transportation: Synergies and Differences Between Cars and Planes

Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible