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.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

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 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 Traffic Conditions and Route Choice of Road Users Between Two Roundabouts

2020 ◽  
Vol 15 (3) ◽  
pp. 446-450
Author(s):  
Lin Zarni Win ◽  
Kyaing ◽  
Ko Ko Lwin ◽  
Yoshihide Sekimoto ◽  
◽  
...  
Keyword(s):  
Government Policy ◽  
Traffic Congestion ◽  
Route Choice ◽  
Policy Makers ◽  
The Road ◽  
Traffic Conditions ◽  
Road Users ◽  
Interview Methods ◽  
The Government ◽  
Almost All

This study aims to present the traffic conditions of one of the most congested areas in Yangon as well as the route choice behaviors of the road users in that area. It analyzes drivers’ route choice behaviors and traffic congestion according to road segments. Manual traffic counting and roadside interview methods were used in this survey. The data gathered were used in finding routes alternative to the U Htaung Bo road, which is extremely congested almost all the time. With regard to the report, it will be helpful to identify the scale of the problem that is caused by traffic congestion and to increase awareness of this issue, including amongst the government, policy makers, traffic engineers, and road users.

scholarly journals Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review

2019 ◽  
Vol 11 (23) ◽  
pp. 6713 ◽  
Author(s):  
Roja Ezzati Amini ◽  
Christos Katrakazas ◽  
Constantinos Antoniou
Keyword(s):  
Decision Making ◽  
Holistic Approach ◽  
Automated Driving ◽  
The Road ◽  
Communication Techniques ◽  
Road Users ◽  
Interaction Modeling ◽  
Pedestrian Crossing ◽  
Near Future ◽  
Road Crossing

The interaction among pedestrians and human drivers is a complicated process, in which road users have to communicate their intentions, as well as understand and anticipate the actions of users in their vicinity. However, road users still ought to have a proper interpretation of each others’ behaviors, when approaching and crossing the road. Pedestrians, as one of the interactive agents, demonstrate different behaviors at road crossings, which do not follow a consistent pattern and may vary from one situation to another. The presented inconsistency and unpredictability of pedestrian road crossing behaviors may thus become a challenge for the design of emerging technologies in the near future, such as automated driving system (ADS). As a result, the current paper aims at understanding the effectual communication techniques, as well as the factors influencing pedestrian negotiation and decision-making process. After reviewing the state-of-the-art and identifying research gaps with regards to vehicle–pedestrian crossing encounters, a holistic approach for road crossing interaction modeling is presented and discussed. It is envisioned that the presented holistic approach will result in enhanced safety, sustainability, and effectiveness of pedestrian road crossings.

Inclusive Design Guidance: External Autonomous Vehicle Interfaces

2020 ◽  
Vol 64 (1) ◽  
pp. 1054-1058
Author(s):  
Karina A. Roundtree ◽  
Steven Hattrup ◽  
Janani Swaminathan ◽  
Nicholas Zerbel ◽  
Jeffrey Klow ◽  
...  
Keyword(s):  
Older Adults ◽  
Autonomous Vehicles ◽  
Cognitive Impairments ◽  
Autonomous Vehicle ◽  
Effective Communication ◽  
Inclusive Design ◽  
Environmental Constraints ◽  
Road Users ◽  
Near Future ◽  
Design Guidance

Autonomous vehicles are expected on roads in the near future and need to interact safely with external road users, such as manual motor drivers, cyclists, and pedestrians. The particular needs of the external road users, such as children, adults, older adults, and individuals with visual, auditory, and/or cognitive impairments, will vary greatly and must be considered in order to design effective inclusive interfaces for all users. Current interface designs lack effective communication between an autonomous vehicle and external road users with regard to conveying and understanding the mobility intent of each party. The goal is to provide inclusive design guidance for an external human-vehicle interface that enables effective communication between autonomous vehicles and external road users. Factors related to communicating intent, the external road users, and environmental constraints, were used to inform the design guidance.

scholarly journals The attachments of ‘autonomous’ vehicles

2021 ◽  
pp. 030631272110387
Author(s):  
Chris Tennant ◽  
Jack Stilgoe
Keyword(s):  
Autonomous Vehicles ◽  
Brute Force ◽  
The Road ◽  
Road Users ◽  
The Social ◽  
The World ◽  
Software And Hardware ◽  
Economic Infrastructure ◽  
On The Road ◽  
The Ideal

The ideal of the self-driving car replaces an error-prone human with an infallible, artificially intelligent driver. This narrative of autonomy promises liberation from the downsides of automobility, even if that means taking control away from autonomous, free-moving individuals. We look behind this narrative to understand the attachments that so-called ‘autonomous’ vehicles (AVs) are likely to have to the world. Drawing on 50 interviews with AV developers, researchers and other stakeholders, we explore the social and technological attachments that stakeholders see inside the vehicle, on the road and with the wider world. These range from software and hardware to the behaviours of other road users and the material, social and economic infrastructure that supports driving and self-driving. We describe how innovators understand, engage with or seek to escape from these attachments in three categories: ‘brute force’, which sees attachments as problems to be solved with more data, ‘solve the world one place at a time’, which sees attachments as limits on the technology’s reach and ‘reduce the complexity of the space’, which sees attachments as solutions to the problems encountered by technology developers. Understanding attachments provides a powerful way to anticipate various possible constitutions for the technology.

scholarly journals Accelerating Learning of Route Choices With C2I: A Preliminary Investigation

2020 ◽  
Author(s):  
Guilherme Santos ◽  
Ana Bazzan
Keyword(s):  
Reinforcement Learning ◽  
Iterative Method ◽  
Learning Process ◽  
Preliminary Investigation ◽  
Experimental Results ◽  
Choice Problem ◽  
The Road ◽  
Road Users ◽  
Infrastructure Network ◽  
Effectiveness And Efficiency

How to choose a route that takes you from A to B? This is an issue that is turning more and more important in modern societies. One way to address this agenda is through the use of communication between the infrastructure (network), and the demand (vehicles). In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users (agents) can accelerate their learning process regarding route choice problem, via reinforcement learning (RL). We employ a microscopic simulator in order to compare our method with two others: RL without communication and an iterative method. Experimental results show that our method outperforms both methods in terms of effectiveness and efficiency.

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