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AbstractPilot projects have emerged in cities globally as a way to experiment with the utilization of a suite of smart mobility and emerging transportation technologies. Automated vehicles (AVs) have become central tools for such projects as city governments and industry explore the use and impact of this emerging technology. This paper presents a large-scale assessment of AV pilot projects in U.S. cities to understand how pilot projects are being used to examine the risks and benefits of AVs, how cities integrate these potentially transformative technologies into conventional policy and planning, and how and what they are learning about this technology and its future opportunities and risks. Through interviews with planning practitioners and document analysis, we demonstrate that the approaches cities take for AVs differ significantly, and often lack coherent policy goals. Key findings from this research include: (1) a disconnect between the goals of the pilot projects and a city's transportation goals; (2) cities generally lack a long-term vision for how AVs fit into future mobility systems and how they might help address transportation goals; (3) an overemphasis of non-transportation benefits of AV pilots projects; (4) AV pilot projects exhibit a lack of policy learning and iteration; and (5) cities are not leveraging pilot projects for public benefits. Overall, urban and transportation planners and decision makers show a clear interest to discover how AVs can be used to address transportation challenges in their communities, but our research shows that while AV pilot projects purport to do this, while having numerous outcomes, they have limited value for informing transportation policy and planning questions around AVs. We also find that AV pilot projects, as presently structured, may constrain planners' ability to re-think transportation systems within the context of rapid technological change.

Pilot projects have emerged in cities globally as a way to experiment with the utilization of a suite of smart mobility and emerging transportation technologies. Automated vehicles (AVs) have become central tools for such projects as city governments and industry explore the use and impact of this emerging technology. This paper presents a large-scale assessment of AV pilot projects in U.S. cities to understand how pilot projects are being used to examine the risks and benefits of AVs, how cities integrate these potentially transformative technologies into conventional policy and planning, and how and what they are learning about this technology and its future opportunities and risks. Through interviews with planning practitioners and document analysis, we demonstrate that the approaches cities take for AVs differ significantly, and often lack coherent policy goals. Key findings from this research include: (1) a disconnect between the goals of the pilot projects and a city's transportation goals; (2) cities generally lack a long-term vision for how AVs fit into future mobility systems and how they might help address transportation goals; (3) an overemphasis of non-transportation benefits of AV pilots projects; (4) AV pilot projects exhibit a lack of policy learning and iteration; and (5) cities are not leveraging pilot projects for public benefits. Overall, urban and transportation planners and decision makers show a clear interest to discover how AVs can be used to address transportation challenges in their communities, but our research shows that while AV pilot projects purport to do this, while having numerous outcomes, they have limited value for informing transportation policy and planning questions around AVs. We also find that AV pilot projects, as presently structured, may constrain planners' ability to re-think transportation systems within the context of rapid technological change.

Local governments play an important role in structuring urban transportation through street design, zoning, and shared jurisdiction over ride-hailing, transit, and road pricing. While cities can harness these powers to steer planning outcomes, there is little research about what local officials think about regulatory changes related to autonomous vehicles (AV). We compile key AV-related policies recommended by scholars but rarely implemented, and conduct a survey of municipal officials throughout the United States, exploring their personal support and perceptions of bureaucratic capacity, legal limits, and political backing for each policy. This paper finds broad personal support for regulations related to right-of-way, equity, and land use, such as for increasing pedestrian space, expanding access for low-income people, and reducing sprawl. However, officials emphasized uncertain bureaucratic and legal capacity for city intervention outside of these areas, reaffirming limited local power in the federal system. Only a minority expected political support for any policy. Greater population size and more liberal resident political ideologies are strongly associated with personal and political support for many policies. Local population growth is correlated with greater capacity to undertake policies. This work contributes to the growing literature on transportation governance in the context of technological uncertainty.

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we cannot disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN's 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applicable data-processing scheme. The steps involved in this pipeline include the adaptation and training of a Residual Convolutional Neural Network to determine a hazard index for each given urban scene, as well as an interpretability analysis based on image segmentation and class activation mapping on those same images. Combined, the outcome of this computational approach is a fine-grained map of hazard levels across a city, and an heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. The proposed framework should be taken as a complement to the work of urban planners and public authorities.

Philip Koopman (Carnegie Mellon University) & William H. Widen (University of Miami - School of Law) have posted A Reasonable Driver Standard for Automated Vehicle Safety on SSRN. Here is the abstract: Current "safe enough" Autonomous Vehicle (AV) metrics focus...

Governments invest a lot of money in the road network and the automated vehicle will play a role in the future. The research question is: How can the current investments in the road network be made sustainable for the introduction of automated vehicles? The communication between cars and infrastructure will be with WI-FI-P and 5G communication. Traffic signs will disappear but not all traffic signs be removed. The road authority probably will invest less in physical and more in the digital infrastructure. Telecom providers will play a role in the digital infrastructure. Interviews with experts are combined with the literature to study case studies. The first case is about the enforcement. The police should invest in WI-FI-P equipment to communicate with automated vehicles. The second case is the Flemish traffic sign database. To keep it up-to-date there is need of a rapid response team. Intersections will change a lot, there will be need for a small server at each intersection to receive the signals from the vehicles. The fourth case is about governmental asset management. Governments should be responsive against quicker response times for maintenance. Another case is about road works. The role of the traffic centre will play a role in it by communicating directly to the vehicles. Overall, it is important to invest in a central database where different stakeholders can tap in for different purposes. Governments should invest in in-car systems used by WI-FI-P and 5G. It is less useful to invest in roadside systems.

Will the Transportation Revolutions Improve Our Lives-or Make Them Worse? -- Electric Vehicles: Approaching the Tipping Point -- Shared Mobility: The Potential of Ridehailing and Pooling -- Vehicle Automation: Our Best Shot at a Transportation Do-Over? -- Upgrading Transit for the Twenty-First Century -- Bridging the Gap between Mobility Haves and Have-Nots -- Remaking the Auto Industry -- The Dark Horse: Will China Win the Electric, Automated, Shared Mobility Race?

The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 Research and Innovation programme/ ERC Grant Agreement n. [833915], project TrafficFluid. ; Summarization: A recently proposed paradigm for vehicular traffic in the era of CAV (connected and automated vehicles), called TrafficFluid, involves lane-free vehicle movement. Lane-free traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity; and this opens the way for consideration of real-time internal boundary control on highways and arterials, in order to flexibly share the total (both directions) road width and capacity among the two directions in dependence of the bi-directional demand and traffic conditions, so as to maximize the total (two directions) flow efficiency. The problem is formulated as a convex QP (Quadratic Programming) problem that may be solved efficiently, and representative case studies shed light on and demonstrate the features, capabilities and potential of the novel control action. ; Presented on: Transportation Research Part C: Emerging Technologies

The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 Research and Innovation programme/ ERC Grant Agreement n. [833915], project TrafficFluid. ; Summarization: This paper develops a path planning algorithm for Connected and Automated Vehicles (CAVs) driving on a lane-free highway, according to a recently proposed novel paradigm for vehicular traffic in the era of CAVs. The approach considers a simple model of vehicle kinematics, along with appropriate constraints for control variables and road boundaries. Appropriate, partly competitive sub-objectives are designed to enable efficient vehicle advancement, while avoiding collisions with other vehicles and infeasible vehicle maneuvers. Based on these elements, a nonlinear Optimal Control Problem (OCP) is formulated for each ego vehicle, and a Feasible Direction Algorithm (FDA) is employed for its computationally efficient numerical solution. The OCP is solved repeatedly for short time horizons within a Model Predictive Control (MPC) framework, while the vehicle advances. It is demonstrated via traffic simulation, involving many such vehicles, on a lane-free ring-road that the proposed approach delivers promising results and can be considered as a candidate for use in further developments related to lane-free CAV traffic. ; Παρουσιάστηκε στο: 24th IEEE International Conference on Intelligent Transportation Systems

Objective To understand the influence of driving experience and distraction on drivers' anticipation of upcoming traffic events in automated vehicles. Background In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.