Do you trust me? Driver responses to automated evasive maneuvers

An increasing number of Conditionally Automated Driving (CAD) systems are being developed by major automotive manufacturers. In a CAD system, the automated system is in control of the vehicle within its operational design domain. Therefore, in CAD the vehicle is capable of tactical control of the vehicle and needs to be able to maneuver evasively by braking or steering to avoid objects. During these evasive maneuvers, the driver may attempt to take back control of the vehicle by intervening. A driver interrupting a CAD vehicle while properly performing an evasive maneuver presents a potential safety risk. To investigate this issue, 36 participants were recruited to participate in a Wizard-of-Oz research study. The participants experienced one of two evasive maneuvers of moderate intensity on a test track. The evasive maneuver required the CAD system to brake or steer to avoid the box placed in the lane of travel of the test vehicle. Drivers glanced toward the obstacle but did not intervene or prepare to intervene in response to the evasive maneuver. Importantly, the drivers who chose to intervene did so safely. These findings suggest that after experiencing a CAD vehicle for a brief period, most participants trusted the system enough to not intervene during a system-initiated evasive maneuver

Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out: Identification of human related risk factors – creation of a taxonomy Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’) Systematic literature search and selection of relevant studies on identified risk factors •Coding of studies •Analysis of risk factors on basis of coded studies •Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk factors were identified as ‘probably not risky’. Risky Probably risky Unclear • Influenced driving – alcohol • Influenced Driving – drugs (legal & illegal) • Speeding and inappropriate speed • Traffic rule violations – red light running • Distraction – cell phone use (hand held) • Distraction – cell phone use (hands free) • Distraction – cell phone use (texting) • Fatigue – sleep disorders – sleep apnea • Risk taking – overtaking • Risk taking – close following behaviour • Insufficient knowledge and skills • Functional impairment – cognitive impairment • Functional impairment – vision loss • Diseases and disorders – diabetes • Personal factors – sensation seeking • Personal factors – ADHD • Emotions – anger, aggression • Fatigue – Not enough sleep/driving while tired • Distraction – conversation with passengers • Distraction – outside of vehicle • Distraction – cognitive overload and inattention • Functional impairment – hearing loss (few studies) • Observation errors (few studies) • Distraction – music – entertainment systems (many studies, mixed results) • Distraction – operating devices (many studies, mixed results) The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)

The future of the urban street in the united states: visions of alternative mobilities in the twenty-first century

This dissertation is concerned with the present and future of urban streets in the United States. The goal is to document and analyze current visions, policies, and strategies related to the form and use of American urban streets. The dissertation examines current mobility trends and offers a framework for organizing visions of the future of urban streets, evaluating them through three lenses: safety, comfort, and delight: assessing physical conditions in accordance with livability standards toward sustainable development. At the same time, it demonstrates the way 12 scenarios (NACTO Blueprint for Autonomous Urbanism, Sidewalk Labs: Quayside Project, Public Square by FXCollaborative, AIANY Future Street, The National Complete Street Coalition, Vision Zero, Smart Columbus, Waymo by Alphabet, The Hyperloop, Tesla “Autopilot,” Ford City of Tomorrow, SOM City of Tomorrow) have intentionally or unintentionally influenced contemporary use of American urban streets. Ultimately, the study shows that while sustainable alternative mobilities continue to emerge, the dominance of the automobility system has led to a stagnation of sustainable urban street development in the United States

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

The White Bicycle: Performance, Installation Art, and Activism in Ghost Bike Memorials

In this project I examine the performative nature of the ghost bike memorial. Ghost bikes, flat-white painted immobile bicycles created by cycling communities and loved ones of victims, are installed roadside to mark the locations of cycling related deaths. Using critical performance ethnography and critical-cultural analysis as methods, I analyze how the ghost bike performs as an artifact of mourning and inspires co-incident performances of grief, activism, and community building and maintenance. As a memorial object used worldwide to represent cycling culture, the ghost bike acts as a social network link that connects a multitude of diverse cycling communities. I present five case studies of ghost bikes in New York City, Durham, North Carolina, Baton Rouge, New Orleans, and Lafayette, Louisiana in order to dissect what the polysemic ghost bike communicates to public audiences. My analysis led to the discovery that ghost bikes are not only used as memorials. They also perform as metonyms for the absent, ruined bodies of cyclists; as markers of racial identity for victims; and as tools to reframe the narratives told about cycling-related deaths. I describe how the differing interpretations of the memorial are adapted to create and alter performances of identity, and I argue for the potential for these performances to influence perceptions about cycling safety, cycling-based legislation, and road infrastructure

Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation