Research on effects and influence by presenting information on priority order and oncoming vehicle to operators for teleoperation of multiple autonomous vehicles

Abstract With self-driving vehicles, it is possible to manage multiple vehicles from a remote location even if one observer does not have a driver in the driver’s seat. Therefore, demonstration experiments are being conducted in various places to remotely monitor two autonomous vehicles and operate them as needed. However, when one observer manages multiple vehicles, the amount of information that can be processed is limited. If we can assist with an appropriate amount of information, we may be able to manage more vehicles. In this study, we perform an experiment in which the priority and the type of assist information are changed and presented to the observer in the overtaking scene of a parked vehicle using a simulator. Focusing on the burden on the observer during remote management of multiple units, the purpose is to identify the information required for monitoring and reduce the burden from changes depending on the type of information to be assisted.

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Multi-Vehicle Planning using RRT-Connect*

Paladyn Journal of Behavioral Robotics ◽

10.2478/s13230-012-0004-5 ◽

2011 ◽

Vol 2 (3) ◽

Cited By ~ 6

Author(s):

Rahul Kala ◽

Kevin Warwick

Keyword(s):

Optimization Algorithm ◽

Relative Position ◽

Autonomous Vehicles ◽

Random Trees ◽

Effective Algorithm ◽

The Road ◽

Travel Plan ◽

Multiple Vehicles ◽

Planning Algorithm ◽

On The Road

AbstractThe problem of planning multiple vehicles deals with the design of an effective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it sub-optimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

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Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

Applied Sciences ◽

10.3390/app11219799 ◽

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.

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Persistent Target Detection and Tracking by an Autonomous Swarm

Volume 5B: 42nd Mechanisms and Robotics Conference ◽

10.1115/detc2018-85573 ◽

2018 ◽

Author(s):

John J. Gainer ◽

Levi D. DeVries ◽

Michael D. Kutzer

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Search Space ◽

Base Station ◽

Radioactive Material ◽

Team Members ◽

Search Area ◽

Multiple Vehicles ◽

Multivehicle Control ◽

Experimental Trials

This paper presents an autonomous multivehicle control algorithm capable of persistently searching and tracking targets in a defined search area subject to operational endurance constraints of individual agents. The algorithm development is modular to allow scalability and a control architecture that can be modified to any type of autonomous vehicle, search area, or target. In practical application, a target can be anything from heat signatures to radioactive material; therefore, this work employs a generic emitter-detector pair as a placeholder relationship for real world applications. The control strategy accounts for the appearance, motion, and disappearance of multiple targets in the search space constituting the utility of creating a team of multiple search agents. When agent battery level drops below a predetermined threshold, the agent returns to a base station to recharge and be relaunched into the mission. Remaining agents must account for this loss and gain of other team members as they exit the search environment. The contributions of this work are 1) the design of search trajectories for autonomous vehicles with limited endurance, 2) incorporation of return-to-base and recharge time requirements, and 3) coordination of multiple vehicles by developing a decision-making model to and assign agents to operational modes. We have run an extensive number of experimental trials to collect and analyze performance data for further development and testing.

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Stain contamination and embedding in electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141986 ◽

1986 ◽

Vol 44 ◽

pp. 50-53

Author(s):

Hilton H. Mollenhauer

Keyword(s):

Electron Microscopy ◽

Information Retrieval ◽

Epoxy Resins ◽

Image Contrast ◽

Sample Preservation ◽

Factors Associated ◽

Amount Of Information ◽

Electron Microscopical ◽

Contrast Information

Many factors (e.g., resolution of microscope, type of tissue, and preparation of sample) affect electron microscopical images and alter the amount of information that can be retrieved from a specimen. Of interest in this report are those factors associated with the evaluation of epoxy embedded tissues. In this context, informational retrieval is dependant, in part, on the ability to “see” sample detail (e.g., contrast) and, in part, on tue quality of sample preservation. Two aspects of this problem will be discussed: 1) epoxy resins and their effect on image contrast, information retrieval, and sample preservation; and 2) the interaction between some stains commonly used for enhancing contrast and information retrieval.

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