scholarly journals Adventure Mode: A Speculative Rideshare Design

2021 ◽  
Vol 3 ◽  
Author(s):  
Stephanie Sherman ◽  
Ash Eliza Smith ◽  
Deborah Forster ◽  
Colleen Emmenegger
Keyword(s):  
Autonomous Vehicles ◽  
Urban Environments ◽  
Human Computer Interactions ◽  
Smart Transportation ◽  
Automotive Company ◽  
Moving Vehicles ◽  
Design Proposal ◽  
And Control ◽  
Speculative Design ◽  
Mode A

Most smart city projections presume efficiency, predictability, and control as core design principles for smart transportation. Adventure Mode is a speculative design proposal developed as part of a research project with a major automotive company that proposes uses and interactions for Autonomous Vehicles (AVs) and rideshare advancements that defy these normative presumptions. Adventure Mode reframes the focus of moving vehicles from destination-based experiences to journey-based ones. Adventure Mode pushes the probabilities for unexpected encounters and anonymous play in increasingly predictable and predicted urban environments. It embraces the submission to algorithmic decision and chance as a ludic modality in human-computer interactions and urban artificial intelligence.

Autonomous Vehicles in the Mobility System

2020 ◽  
pp. 500-514
Author(s):  
Thilo von Pape
Keyword(s):  
Artificial Intelligence ◽  
Mobile Phones ◽  
Mobile Communication ◽  
Autonomous Vehicles ◽  
Communication Research ◽  
Collective Control ◽  
System P ◽  
Mobility System ◽  
And Control ◽  
Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

scholarly journals Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation

2020 ◽  
Vol 53 (2) ◽  
pp. 10861-10866
Author(s):  
Constantin F. Caruntu ◽  
Carlos M. Pascal ◽  
Anca Maxim ◽  
Ovidiu Pauca
Keyword(s):  
Autonomous Vehicles ◽  
And Control ◽  
Coordination And Control

scholarly journals Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4236
Author(s):  
Woosik Lee ◽  
Hyojoo Cho ◽  
Seungho Hyeong ◽  
Woojin Chung
Keyword(s):  
Motion Estimation ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Key Technology ◽  
Satellite Systems ◽  
Agricultural Robots ◽  
Global Navigation Satellite

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

2021 ◽  
Vol 3 (3) ◽  
pp. 164-179
Author(s):  
Haoxiang Wang
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Transportation System ◽  
Traffic Condition ◽  
Smart Transportation ◽  
Centralized Management ◽  
The City

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

Research and Development of CVIS Testing System at Road Intersection Based on ZigBee

2013 ◽  
Vol 462-463 ◽  
pp. 505-509 ◽  
Author(s):  
Hao Zhang
Keyword(s):  
Testing System ◽  
Traffic Light ◽  
Interactive Communication ◽  
Control Center ◽  
Time Period ◽  
Moving Vehicles ◽  
Traffic Light Control ◽  
Infrastructure System ◽  
Feedback Data ◽  
And Control

The traditional traffic light control system which applies timer control cannot adjust the light time period effectively when the vehicle flow changes. Based on the Cooperative Vehicle Infrastructure System (CVIS), this paper puts forward a scheme that traffic light can be changed adaptively according to the vehicle flow. It realizes the interactive communication among moving vehicles, roadside equipments and control center. As a result, the traffic light time period is regulated by the real-time feedback data.

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