scholarly journals Endangering yourself to save another: A real life ethical dilemma

2018 ◽  
Author(s):  
Igor Radun ◽  
Jenni Radun ◽  
Jyrki Kaistinen ◽  
Jake Olivier ◽  
Göran Kecklund ◽  
...  
Keyword(s):  
Ethical Dilemma ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Survey Study ◽  
Heavy Vehicle ◽  
The Road ◽  
Road Users ◽  
Empathy Score ◽  
Experimental Survey

Unlike hypothetical trolley problem studies and an ongoing ethical dilemma with autonomous vehicles, road users can face similar ethical dilemmas in real life. Swerving a heavy vehicle towards the road-side in order to avoid a head-on crash with a much lighter passenger car is often the only option available which could save lives. However, running off-road increases the probability of a roll-over and endangers the life of the heavy vehicle driver. We have created an experimental survey study in which heavy vehicle drivers randomly received one of two possible scenarios. We found that responders were more likely to report they would ditch their vehicle in order to save the hypothetical driver who fell asleep than to save the driver who deliberately diverted their car towards the participant’s heavy vehicle. Additionally, the higher the empathy score, the higher the probability of ditching a vehicle. Implications for autonomous vehicle programming are discussed.

scholarly journals Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 297
Author(s):  
Ali Marzoughi ◽  
Andrey V. Savkin
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Sufficient Conditions ◽  
Real Life ◽  
Unmanned Surface Vehicles ◽  
Necessary And Sufficient ◽  
See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals An Initial Investigation of the Effects of a Fully Automated Vehicle Fleet on Geometric Design

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
John Khoury ◽  
Kamar Amine ◽  
Rima Abi Saad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Geometric Design ◽  
Design Elements ◽  
Sight Distance ◽  
Human Driver ◽  
Projected Change ◽  
Vehicle Fleet ◽  
Stopping Sight Distance

This paper investigates the potential changes in the geometric design elements in response to a fully autonomous vehicle fleet. When autonomous vehicles completely replace conventional vehicles, the human driver will no longer be a concern. Currently, and for safety reasons, the human driver plays an inherent role in designing highway elements, which depend on the driver’s perception-reaction time, driver’s eye height, and other driver related parameters. This study focuses on the geometric design elements that will directly be affected by the replacement of the human driver with fully autonomous vehicles. Stopping sight distance, decision sight distance, and length of sag and crest vertical curves are geometric design elements directly affected by the projected change. Revised values for these design elements are presented and their effects are quantified using a real-life scenario. An existing roadway designed using current AASHTO standards has been redesigned with the revised values. Compared with the existing design, the proposed design shows significant economic and environmental improvements, given the elimination of the human driver.

scholarly journals Contested Spaces, Shared Concerns

2020 ◽  
Vol 9 (2) ◽  
pp. 155-191
Author(s):  
Sarah Stutts ◽  
Kenneth Saintonge ◽  
Nicholas Jordan ◽  
Christina Wasson
Keyword(s):  
Autonomous Vehicles ◽  
Road User ◽  
North Texas ◽  
University Of North Texas ◽  
Contested Spaces ◽  
The Road ◽  
Driving Behaviors ◽  
Road Users ◽  
On The Road ◽  
The University

Roadways are sociocultural spaces constructed for human travel which embody intersections of technology, transportation, and culture. In order to navigate these spaces successfully, autonomous vehicles must be able to respond to the needs and practices of those who use the road. We conducted research on how cyclists, solid waste truck drivers, and crossing guards experience the driving behaviors of other road users, to inform the development of autonomous vehicles. We found that the roadways were contested spaces, with each road user group enacting their own social constructions of the road. Furthermore, the three groups we worked with all felt marginalized by comparison with car drivers, who were ideologically and often physically dominant on the road. This article is based on research for the Nissan Research Center - Silicon Valley, which took place as part of a Design Anthropology course at the University of North Texas.

scholarly journals Using Car to Infrastructure Communication to Accelerate Learning in Route Choice

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Guilherme D. dos Santos ◽  
Ana L. C. Bazzan ◽  
Arthur Prochnow Baumgardt
Keyword(s):  
Autonomous Vehicles ◽  
Metropolitan Areas ◽  
Route Choice ◽  
Learning Processes ◽  
Traffic Situation ◽  
The Road ◽  
Road Users ◽  
Learning Tasks ◽  
Infrastructure Network ◽  
Near Future

The task of choosing a route to move from A to B is not trivial, as road networks in metropolitan areas tend to be over crowded. It is important to adapt on the fly to the traffic situation. One way to help road users (driver or autonomous vehicles for that matter) is by using modern communication technologies.In particular, there are reasons to believe that the use of communication between the infrastructure (network), and the demand (vehicles) will be a reality in the near future. In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users can accelerate their learning processes regarding route choice by using reinforcement learning (RL). The kernel of our method is a two way communication, where road users communicate their rewards to the infrastructure, which, in turn, aggregate this information locally and pass it to other users, in order to accelerate their learning tasks. We employ a microscopic simulator in order to compare this method with two others (one based on RL without communication and a classical iterative method for traffic assignment). Experimental results using a grid and a simplification of a real-world network show that our method outperforms both.

scholarly journals Self-protective and self-sacrificing preferences of pedestrians and passengers in moral dilemmas involving autonomous vehicles

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261673
Author(s):  
Maike M. Mayer ◽  
Raoul Bell ◽  
Axel Buchner
Keyword(s):  
Social Desirability ◽  
Autonomous Vehicles ◽  
Critical Incidents ◽  
Autonomous Vehicle ◽  
Moral Dilemmas ◽  
Road Users ◽  
Course Of Action ◽  
Moral Decisions ◽  
Conflicting Interests ◽  
Accident Scenarios

Upon the introduction of autonomous vehicles into daily traffic, it becomes increasingly likely that autonomous vehicles become involved in accident scenarios in which decisions have to be made about how to distribute harm among involved parties. In four experiments, participants made moral decisions from the perspective of a passenger, a pedestrian, or an observer. The results show that the preferred action of an autonomous vehicle strongly depends on perspective. Participants’ judgments reflect self-protective tendencies even when utilitarian motives clearly favor one of the available options. However, with an increasing number of lives at stake, utilitarian preferences increased. In a fifth experiment, we tested whether these results were tainted by social desirability but this was not the case. Overall, the results confirm that strong differences exist among passengers, pedestrians, and observers about the preferred course of action in critical incidents. It is therefore important that the actions of autonomous vehicles are not only oriented towards the needs of their passengers, but also take the interests of other road users into account. Even though utilitarian motives cannot fully reconcile the conflicting interests of passengers and pedestrians, there seem to be some moral preferences that a majority of the participants agree upon regardless of their perspective, including the utilitarian preference to save several other lives over one’s own.

scholarly journals Road Segmentation using Semantic Segmentation Networks for ADAS

2019 ◽  
Vol 8 (11) ◽  
pp. 1740-1743
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Semantic Segmentation ◽  
Boundary Line ◽  
Segmentation Result ◽  
Safe Driving ◽  
The Road ◽  
Straight Line ◽  
Road Segmentation

In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system

scholarly journals Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

2020 ◽  
Author(s):  
Fanta Camara ◽  
Charles Fox
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Utility Functions ◽  
Hyperbolic Function ◽  
The Road ◽  
Definition Of ◽  
Public Datasets ◽  
Discrete Theory ◽  
And Control ◽  
Road Crossing

AbstractUnderstanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

Virtual Simulations Environment Development for Autonomous Vehicles Interaction

2020 ◽  
Author(s):  
Mohsen Malayjerdi ◽  
Vladimir Kuts ◽  
Raivo Sell ◽  
Tauno Otto ◽  
Barış Cem Baykara
Keyword(s):  
Virtual Environment ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Human Robot Interaction ◽  
Industrial Robots ◽  
Simulation Environment ◽  
Road Users ◽  
Virtual Simulations ◽  
Road Testing

Abstract One of the primary verification criteria of the autonomous vehicle is safe interaction with other road users. Based on studies, real-road testing is not practical for safety validation due to its time and cost consuming. Therefore, simulating miles driven is the only feasible way to overcome this limitation. The primary goal of the related research project is to develop advanced techniques in the human-robot interaction (HRI) safety validation area by usage of immersive simulation technologies. Developing methods for the creation of the simulation environment will enable us to do experiments in a digital environment rather than real. The main aim of the paper is to develop an effective method of creating a virtual environment for performing simulations on industrial robots, mobile robots, and autonomous vehicles (AGV-s) from the safety perspective for humans. A mid-size drone was used for aerial imagery as the first step in creating a virtual environment. Then all the photos were processed in several steps to build the final 3D map. Next, this mapping method was used to create a high detail simulation environment for testing an autonomous shuttle. Developing efficient methods for mapping real environments and simulating their variables is crucial for the testing and development of control algorithms of autonomous vehicles.

Sign in / Sign up

Export Citation Format

Share Document