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

Evaluating interactions of task relevance and visual attention in driver multitasking

Use of cellular phones while driving, and safety implications thereof, has captured public and scientific interest. Previous research has shown that driver reactions and attention are impacted by cellular phone use. Generally, previous research studies have not focused on how visual attention and driver performance may interact. Strayer and colleagues found lower recognition for items present in the driving environment when drivers were using a cellular phone than when not using the phone; however, the tested items were not directly relevant to driving. Relevance to driving may have an impact on attention allocation. The current project used a mediumidelity driving simulator to extend previous research in two ways: 1) how attention is allocated across driving-relevant and -irrelevant items in the environment was investigated, and 2) driving performance measures and eye movement measures were considered together rather than in isolation to better illustrate the impact of cellular phone distraction on driver behavior. Results from driving performance measures replicated previous findings that vehicle control is negatively impacted by driver distraction. Interestingly, there were no interactions of relevance and distraction found, suggesting that participants responded to potential hazards similarly in driving-only and distraction conditions. In contrast to previous research, eye movement patterns (primarily measured by number of gazes) were impacted by distraction. Gaze patterns differed across relevance levels, with hazards receiving the most gazes, and signs receiving the fewest. The relative size of the critical items may have impacted gaze probability in this relatively undemanding driving environment. In contrast to the driving performance measures, the eye movement measures did show an interaction between distraction and relevance; thus, eye movements may be a more direct and more sensitive measure of driver attention. Recognition memory results were consistently near chance performance levels and did not reflect the patterns found in the eye movement or driving performance measures

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166283/1/itr2bf00581.pd

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information

Mitigations to Reduce the Law of Unintended Consequences for Autonomy and Other Technological Advances

The United Nations states that Earths population is expected to reach just under 10 billion people (9.7) by the year 2050. To meet the demands of 10 billion people, governments, multinational corporations and global leaders are relying on autonomy and technological advances to augment and/or accommodate human efforts to meet the required needs of daily living. Genetically modified organisms (GMOs), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene-edited plants and cloning will be utilized to expand human food supply. Biomimetic implants are expected to improve life expectancy with 3D printed body parts. Human functioning will be extended with wearables and cybernetic implants continuing humanitys path toward transhumanism. Families will be strengthened with 3 parent households. Disease will surely be eradicated using the CRISPR-CAS9 genetic engineering revolution to design out undesirable human traits and to design in new capabilities. With autonomous cars, trucks and buses on our roads and on-demand autonomous aircraft delivering pizzas, medical prescriptions and groceries in the air and multi-planet vehicles traversing space, utopia will finally arrive! Or will it? All of these powerful, man-made, technological systems will experience unintended consequences with certainty. Instead of over-reacting with hysteria and fear, we should be seeking answers to the following questions - What skills are required to architect socially-healthy technological systems for 2050? What mindsets should we embody to ameliorate hubris syndrome and to build our future technological systems with deliberation, soberness and social responsibility

What happens when drivers face hazards on the road?

The current study aims to obtain knowledge about the nature of the processes involved in Hazard Perception, using measurement techniques to separate and independently quantify these suspected sub-processes: Sensation, Situation Awareness (recognition, location and projection) and Decision-Making. It applies Signal Detection Theory analysis to Hazard Perception and Prediction Tasks. To enable the calculation of Signal Detection Theory parameters, video-recorded hazardous vs. quasi-hazardous situations were presented to the participants. In the hazardous situations it is necessary to perform an evasive action, for instance, braking or swerving abruptly, while the quasi-hazardous situations do not require the driver to make any evasive manoeuvre, merely to carry on driving at the same speed and following the same trajectory. A first Multiple Choice Hazard Perception and Prediction test was created to measure participants’ performance in a What Happens Next? Task. The sample comprised 143 participants, 47 females and 94 males. Groups of non-offender drivers (learner, novice and experienced) and offender drivers (novice and experienced) were recruited. The Multiple Choice Hazard Perception and Prediction test succeeded in finding differences between drivers according to their driving experience. In fact, differences exist with regard to the level of hazard discrimination (d’ prime) by drivers with different experience (learner, novice and experienced drivers) and profile (offenders and non-offenders) and these differences emerge from Signal Detection Theory analysis. In addition, it was found that experienced drivers show higher Situation Awareness than learner or novice drivers. On the other hand, although offenders do worse than non-offenders on the hazard identification question, they do just as well when their Situation Awareness is probed (in fact, they are as aware as non-offenders of what the obstacles on the road are, where they are and what will happen next). Nevertheless, when considering the answers participants provided about their degree of cautiousness, experienced drivers were more cautious than novice drivers, and non-offender drivers were more cautious than offender drivers. That is, a greater number of experienced and non-offender drivers chose the answer “I would make an evasive manoeuvre such as braking gradually”