Automated drivability: Toward an assessment of the spatial deployment of level 4 automated vehicles

Abstract Two scenarios have shaped the discussion on the deployment of automated vehicles (AVs). The first is the revolutionary or disruptive scenario, in which a competitor would reach fully automated vehicles (level 5) in one giant leap. The second scenario renders the deployment of AVs as a lengthy process of evolutionary vehicle automation, progressing from specialized, controlled and restricted conditions, i.e. operational design domains (ODDs), to ever more complex ones. But as it becomes more and more clear that there are large uncertainties about how far in the future level 5 AVs become available, in the near future situations in which the driver is replaced completely, i.e. not requiring driver interventions, only seem feasible in specific ODDs and cities will likely be faced by highly automated vehicles (level 4) equipped with automated driving systems (ADSs) for specific ODDs over a longer period. However, cities comprise different street spaces with various conditions which lead to different requirements for the ADSs and could affect the deployment of level 4 AVs and thus the impacts of AVs in cities in the near future. This paper presents a framework and related indicators for assessing the automated drivability, i.e. the suitability of street spaces for the functioning operation of ADSs from a technical-infrastructural point of view, based on the relationship between the current technological development state of ADSs and different (urban) street spaces. Using the case study of the city of Vienna in Austria to apply the framework, a set of indicators for the different components of the framework is calculated and integrated to build an automated drivability index (ADX), illustrating the automated drivability within the street network. The results are a first step to indicate which areas are more suitable for level 4 AVs from a technological point of view, as well as areas where a deployment of AVs would only be possible after larger adjustments of the infrastructure or at considerably lower than intended speeds; thus, detailing the complexity of driving urban street spaces in a yet unprecedented level. Besides other aspects this can give hints for policy makers and other stakeholders where to facilitate the deployment of AVs.

[1]  Joern Birkmann,et al.  Indicators and criteria for measuring vulnerability : theoretical bases and requirements , 2006 .

[2]  Henry Prakken,et al.  On the problem of making autonomous vehicles conform to traffic law , 2017, Artificial Intelligence and Law.

[3]  Elizabeth Burton,et al.  Measuring Urban Compactness in UK Towns and Cities , 2002 .

[4]  Steven E. Shladover,et al.  Connected and automated vehicle systems: Introduction and overview , 2018, J. Intell. Transp. Syst..

[5]  Lee Skrypchuk,et al.  Analysis of autopilot disengagements occurring during autonomous vehicle testing , 2018, IEEE/CAA Journal of Automatica Sinica.

[6]  Itf.  Safer Roads with Automated Vehicles , 2018 .

[7]  R. van Nes,et al.  Assessing the travel impacts of subnetworks for automated driving: An exploratory study , 2019, Case Studies on Transport Policy.

[8]  C. Nykiforuk,et al.  Check the score: Field validation of Street Smart Walk Score in Alberta, Canada , 2016, Preventive medicine reports.

[9]  Pedro J. Navarro,et al.  A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research , 2019, Sensors.

[10]  C Johnson,et al.  Readiness of the road network for connected and autonomous vehicles , 2017 .

[11]  Desta Haileselassie Hagos,et al.  A Crowd-Based Intelligence Approach for Measurable Security, Privacy, and Dependability in Internet of Automated Vehicles with Vehicular Fog , 2018, Mob. Inf. Syst..

[12]  Nazanin Nader,et al.  Autonomous vehicles' disengagements: Trends, triggers, and regulatory limitations. , 2018, Accident; analysis and prevention.

[13]  Meghan Winters,et al.  Mapping Bikeability: A Spatial Tool to Support Sustainable Travel , 2013 .

[14]  Nazanin Nader,et al.  Examining accident reports involving autonomous vehicles in California , 2017, PloS one.

[15]  Francesco Ciari,et al.  Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies , 2018, Transport Reviews.

[16]  Miles Tight,et al.  A comparison of three methods for assessing the walkability of the pedestrian environment , 2011 .

[17]  Marcelo H. Ang,et al.  Perception, Planning, Control, and Coordination for Autonomous Vehicles , 2017 .

[18]  Behram Wali,et al.  Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach. , 2019, Accident; analysis and prevention.

[19]  F. Moura,et al.  Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon , 2017 .

[20]  Matthew Johnson-Roberson,et al.  Failing to Learn: Autonomously Identifying Perception Failures for Self-Driving Cars , 2017, IEEE Robotics and Automation Letters.

[21]  Magnus Egerstedt,et al.  Autonomous driving in urban environments: approaches, lessons and challenges , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Hermann Winner,et al.  Use Cases for Autonomous Driving , 2016 .

[23]  Shiliang Su,et al.  Auditing street walkability and associated social inequalities for planning implications , 2019, Journal of Transport Geography.

[24]  Steven E Shladover,et al.  Development of California Regulations to Govern Testing and Operation of Automated Driving Systems , 2015 .

[25]  Sven Beiker.  Deployment of Automated Driving as an Example for the San Francisco Bay Area , 2019 .

[26]  Haneen Farah,et al.  Infrastructure for Automated and Connected Driving: State of the Art and Future Research Directions , 2018 .

[27]  Madjid Tavana,et al.  Autonomous vehicles: challenges, opportunities, and future implications for transportation policies , 2016, Journal of Modern Transportation.

[28]  Homayoun Najjaran,et al.  Autonomous vehicle perception: The technology of today and tomorrow , 2018 .

[29]  David Metz,et al.  Developing Policy for Urban Autonomous Vehicles: Impact on Congestion , 2018 .

[30]  Sebastian Thrun,et al.  Toward robotic cars , 2010, CACM.

[31]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[32]  Emilio Frazzoli,et al.  A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles , 2016, IEEE Transactions on Intelligent Vehicles.