scholarly journals Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

2021 ◽  
Vol 11 (21) ◽  
pp. 9799
Author(s):  
Syed Qamar Zulqarnain ◽  
Sanghwan Lee
Keyword(s):  
Remote Control ◽  
Autonomous Vehicles ◽  
Fuel Efficiency ◽  
Traffic Situation ◽  
Human Intervention ◽  
Communication Latency ◽  
Road Capacity ◽  
Multiple Vehicles ◽  
Remote Driving ◽  
Better Than

These days, autonomous vehicles (AVs) technology has been improved dramatically. However, even though the AVs require no human intervention in most situations, AVs may fail in certain situations. In such cases, it is desirable that humans can operate the vehicle manually to recover from a failure situation through remote driving. Furthermore, we believe that remote driving can enhance the current transportation system in various ways. In this paper, we consider a revolutionary transportation platform, where all the vehicles in an area are controlled by some remote controllers or drivers so that transportation can be performed in a more efficient way. For example, road capacity can be effectively utilized and fuel efficiency can be increased by centralized remote control. However, one of the biggest challenges in such remote driving is the communication latency between the remote driver and the vehicle. Thus, selecting appropriate locations of the remote drivers is very important to avoid any type of safety problem that might happen due to large communication latency. Furthermore, the selection should reflect the traffic situation created by multiple vehicles in an area. To tackle these challenges, in this paper, we propose several algorithms that select remote drivers’ locations for a given transportation schedules of multiple vehicles. We consider two objectives in this system and evaluate the performance of the proposed algorithms through simulations. The results show that the proposed algorithms perform better than some baseline algorithms.

Situation awareness in remote operators of autonomous vehicles: Developing a taxonomy of situation awareness in video-relays of driving scenes

2021 ◽  
Author(s):  
clare mutzenich ◽  
Szonya Durant ◽  
Shaun Helman ◽  
Polly Dalton
Keyword(s):  
Human Factors ◽  
Situation Awareness ◽  
Mental Model ◽  
Autonomous Vehicles ◽  
Human Intervention ◽  
Novel Approach ◽  
Video Feed ◽  
Remote Environment ◽  
Verbal Data ◽  
Remote Driving

Even entirely driverless vehicles will sometimes require remote human intervention. Existing SA frameworks do not acknowledge the significant human factors challenges unique to a driver in charge of a vehicle that they are not physically occupying. Remote operators will have to build up a mental model of the remote environment facilitated by monitor view and video feed. We took a novel approach to 'freeze and probe' techniques to measure SA, employing a qualitative verbal elicitation task to uncover what people ‘see’ in a remote scene when they are not constrained by rigid questioning. Participants (n=10) watched eight videos of driving scenes randomised and counterbalanced across four road types (motorway, rural, residential and A road). Participants recorded spoken descriptions when each video stopped, detailing what was happening (comprehension) and what could happen next (prediction). Participant transcripts provided a rich catalogue of verbal data reflecting clear interactions between different SA levels. This suggests that acquiring SA in remote scenes is a flexible and fluctuating process of combining comprehension and prediction globally rather than serially, in contrast to what has sometimes been implied by previous SA methodologies (Endsley, 2000; Endsley, 2017; Jones & Endsley, 1996). Participants’ responses were also categorised to form a ‘Taxonomy of SA’ aimed at capturing the key elements of people’s reported SA for videos of driving situations. We suggest that existing theories of SA need to be more sensitively applied to remote driving contexts such as remote operators of autonomous vehicles.

scholarly journals Road Capacity and Throughput for Safe Driving Autonomous Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95779-95792
Author(s):  
Yuan-Ying Wang ◽  
Hung-Yu Wei
Keyword(s):  
Autonomous Vehicles ◽  
Safe Driving ◽  
Road Capacity

scholarly journals CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 971-980 ◽  
Author(s):  
Michal Maciejewski ◽  
Joschka Bischoff
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Traffic Volume ◽  
City Centre ◽  
Conventional Vehicle ◽  
Road Capacity ◽  
Private Car ◽  
The One ◽  
The Impact

Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.

Research on Road Capacity in the Scenarios of Autonomous Vehicles in China

2020 ◽  
Author(s):  
Jing Chen ◽  
Jian Li ◽  
Ning Zhang ◽  
Xi Zhuo ◽  
Yuchuan Du
Keyword(s):  
Autonomous Vehicles ◽  
Road Capacity

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 Quick Routing in Autonomous Vehicles using Boosting Algorithms

2019 ◽  
Vol 9 (1S3) ◽  
pp. 539-542
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fuel Efficiency ◽  
Time Span ◽  
Minimum Time ◽  
Novel Technique ◽  
Boosting Algorithms

Self-driving automobiles are understandably the most attention grabbing utility of artificial intelligence. Until recently, we have just considered the prototypes of these cars in Sci-fi movies, with the whole thing else left to our imagination. But with advances in technology, this super notion has acquired a lifestyles of its own. Autonomous vehicle promises to improve traffic safety while at the same time, it must increase the fuel efficiency, reduce congestion and arrive to the destination at a minimum time span. We propose a novel technique to boost the algorithm to take the shortest path while the vehicle is in movement.

Autonomous Vehicular Corridor using Artificial Intelligence

2021 ◽  
Vol 9 (VI) ◽  
pp. 1254-1258
Author(s):  
Aravind R Kashyap
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Human Intervention ◽  
Macroscopic Level ◽  
Operational Impact

This project considers the operational impact of Autonomous Vehicles by creating a corridor using the latest network available. The behaviour of these vehicles entering the corridor is monitored at the macroscopic level by modifying the data which can be extracted from the vehicle. This data is made to learn using machine learning called the Time Series Neural Network and the data is used as a parameter to make the vehicles Autonomous. The project resolves the location, develops and demonstrates the collision avoidance of the vehicles using Artificial Intelligence. Autonomous means the vehicles will be able to learn to act accordingly without human intervention

scholarly journals Evaluation of XcalableACC with tightly coupled accelerators/InfiniBand hybrid communication on accelerated cluster

2019 ◽  
Vol 33 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Masahiro Nakao ◽  
Tetsuya Odajima ◽  
Hitoshi Murai ◽  
Akihiro Tabuchi ◽  
Norihisa Fujita ◽  
...  
Keyword(s):  
Programming Language ◽  
High Performance ◽  
Lattice Quantum Chromodynamics ◽  
Cluster Systems ◽  
Communication Latency ◽  
Lattice Quantum ◽  
Tightly Coupled ◽  
High Bandwidth ◽  
Sequential Code ◽  
Better Than

Accelerated clusters, which are cluster systems equipped with accelerators, are one of the most common systems in parallel computing. In order to exploit the performance of such systems, it is important to reduce communication latency between accelerator memories. In addition, there is also a need for a programming language that facilitates the maintenance of high performance by such systems. The goal of the present article is to evaluate XcalableACC (XACC), a parallel programming language, with tightly coupled accelerators/InfiniBand (TCAs/IB) hybrid communication on an accelerated cluster. TCA/IB hybrid communication is a combination of low-latency communication with TCA and high bandwidth with IB. The XACC language, which is a directive-based language for accelerated clusters, enables programmers to use TCA/IB hybrid communication with ease. In order to evaluate the performance of XACC with TCA/IB hybrid communication, we implemented the lattice quantum chromodynamics (LQCD) mini-application and evaluated the application on our accelerated cluster using up to 64 compute nodes. We also implemented the LQCD mini-application using a combination of CUDA and MPI (CUDA + MPI) and that of OpenACC and MPI (OpenACC + MPI) for comparison with XACC. Performance evaluation revealed that the performance of XACC with TCA/IB hybrid communication is 9% better than that of CUDA + MPI and 18% better than that of OpenACC + MPI. Furthermore, the performance of XACC was found to further increase by 7% by new expansion to XACC. Productivity evaluation revealed that XACC requires much less change from the serial LQCD code to implement the parallel LQCD code than CUDA + MPI and OpenACC + MPI. Moreover, since XACC can perform parallelization while maintaining the sequential code image, XACC is highly readable and shows excellent portability due to its directive-based approach.

scholarly journals A Fourier transform spectroradiometer for ground-based remote sensing of the atmospheric downwelling long-wave radiance

2019 ◽  
Vol 12 (1) ◽  
pp. 619-635 ◽  
Author(s):  
Giovanni Bianchini ◽  
Francesco Castagnoli ◽  
Gianluca Di Natale ◽  
Luca Palchetti
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Remote Control ◽  
High Altitude ◽  
Far Infrared ◽  
Balloon Flight ◽  
Long Wave ◽  
Stratospheric Balloon ◽  
Spectrally Resolved ◽  
Better Than

Abstract. The Radiation Explorer in the Far Infrared – Prototype for Applications and Development (REFIR-PAD) is a Fourier transform spectroradiometer that has been designed to operate from both stratospheric balloon platforms and the ground. It has been successfully deployed in a stratospheric balloon flight and several ground-based campaigns from high-altitude sites, including the current installation at the Italian–French Concordia Antarctic station. The instrument is capable of operating autonomously with only a limited need of remote control and monitoring and provides a multiyear dataset of spectrally resolved atmospheric downwelling radiances, measured in the 100–1500 cm−1 spectral range with 0.4 cm−1 resolution and a radiometric uncertainty of better than 0.85 mW(m2srcm-1)-1.

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