A Location Algorithm for Autonomous Vehicles in Large-scale Scenarios Using a Multilayer LIDAR

2020 ◽  
Author(s):  
Xiao Zhou ◽  
Zhen Jiang ◽  
Qi Li
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Location Algorithm

High-precision Shack-Hartmann wavefront sensing with a multi-focal diffraction Taiji-lenslet array

Laser Physics ◽  
2021 ◽  
Vol 31 (12) ◽  
pp. 125401
Author(s):  
Yaling Yang ◽  
Yanli Zhang ◽  
Junyong Zhang ◽  
You Li ◽  
Dean Liu
Keyword(s):  
Large Scale ◽  
Measurement Accuracy ◽  
Laser Beams ◽  
Diffractive Lens ◽  
Wavefront Sensors ◽  
Entrance Pupil ◽  
Optical Elements ◽  
Lenslet Array ◽  
Location Algorithm ◽  
Detection Instrument

Abstract A Hartmann wavefront sensor is a type of wavefront detection instrument that has been widely used in various fields. Traditional Hartmann wavefront sensors usually comprise a monofocal refraction lenslet array to segment the wavefront at the entrance pupil. Each wavelet is focused at the focal plane along the projection of the lenslet, forming the foci array. Unlike the multifocal self-interference Taiji-lenslet array, a type of multifocal diffraction Taiji-lenslet array was proposed in this study to improve the measurement accuracy using the weighted centroid location algorithm of these multifocal spots, where the latter is more easily designed than the former. An optical experiment was implemented using the multifocal diffraction Taiji-lenslet array to verify its effectiveness. As a type of diffractive lens, a large-aperture Taiji-lenslet array can be easily fabricated via lithography, which has great potential for application in the measurement of large-scale laser beams and optical elements.

YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study

2019 ◽  
Vol 2673 (8) ◽  
pp. 242-253 ◽  
Author(s):  
Subasish Das ◽  
Anandi Dutta ◽  
Tomas Lindheimer ◽  
Mohammad Jalayer ◽  
Zachary Elgart
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automotive Industry ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Keyword Search ◽  
Autonomous Vehicle ◽  
Perception Of Safety ◽  
Automation Level

The automotive industry is currently experiencing a revolution with the advent and deployment of autonomous vehicles. Several countries are conducting large-scale testing of autonomous vehicles on private and even public roads. It is important to examine the attitudes and potential concerns of end users towards autonomous cars before mass deployment. To facilitate the transition to autonomous vehicles, the automotive industry produces many videos on its products and technologies. The largest video sharing website, YouTube.com, hosts many videos on autonomous vehicle technology. Content analysis and text mining of the comments related to the videos with large numbers of views can provide insight about potential end-user feedback. This study examines two questions: first, how do people view autonomous vehicles? Second, what polarities exist regarding (a) content and (b) automation level? The researchers found 107 videos on YouTube using a related keyword search and examined comments on the 15 most-viewed videos, which had a total of 60.9 million views and around 25,000 comments. The videos were manually clustered based on their content and automation level. This study used two natural language processing (NLP) tools to perform knowledge discovery from a bag of approximately seven million words. The key issues in the comment threads were mostly associated with efficiency, performance, trust, comfort, and safety. The perception of safety and risk increased in the textual contents when videos presented full automation level. Sentiment analysis shows mixed sentiments towards autonomous vehicle technologies, however, the positive sentiments were higher than the negative.

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.

Coordinated Model Predictive Control on Multi-Lane Roads

2019 ◽  
Author(s):  
Nathan Goulet ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Heterogeneous Traffic ◽  
Road Transportation ◽  
The Road ◽  
Energy Saving Potential ◽  
Coordination Scheme ◽  
Predictive Controller

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

2020 ◽  
Vol 2674 (11) ◽  
pp. 532-541 ◽  
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder ◽  
Andreas Rau
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Public Transit ◽  
Programming Model ◽  
Real Life ◽  
Single Line ◽  
Continuum Approximation ◽  
Transit System ◽  
Fleet Size ◽  
Minimum Number

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

scholarly journals Toward a rational and ethical sociotechnical system of autonomous vehicles: A novel application of multi-criteria decision analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256224
Author(s):  
Veljko Dubljevic ◽  
George List ◽  
Jovan Milojevich ◽  
Nirav Ajmeri ◽  
William A. Bauer ◽  
...  
Keyword(s):  
Decision Analysis ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Status Quo ◽  
Regulatory Control ◽  
Sociotechnical System ◽  
Multi Criteria Decision Analysis ◽  
Reliable Assessment ◽  
The Status ◽  
Potential Benefits

The impacts of autonomous vehicles (AV) are widely anticipated to be socially, economically, and ethically significant. A reliable assessment of the harms and benefits of their large-scale deployment requires a multi-disciplinary approach. To that end, we employed Multi-Criteria Decision Analysis to make such an assessment. We obtained opinions from 19 disciplinary experts to assess the significance of 13 potential harms and eight potential benefits that might arise under four deployments schemes. Specifically, we considered: (1) the status quo, i.e., no AVs are deployed; (2) unfettered assimilation, i.e., no regulatory control would be exercised and commercial entities would “push” the development and deployment; (3) regulated introduction, i.e., regulatory control would be applied and either private individuals or commercial fleet operators could own the AVs; and (4) fleets only, i.e., regulatory control would be applied and only commercial fleet operators could own the AVs. Our results suggest that two of these scenarios, (3) and (4), namely regulated privately-owned introduction or fleet ownership or autonomous vehicles would be less likely to cause harm than either the status quo or the unfettered options.

Integrating Supply and Demand Perspectives for a Large-Scale Simulation of Shared Autonomous Vehicles

2020 ◽  
Vol 2674 (7) ◽  
pp. 181-192 ◽  
Author(s):  
Krishna Murthy Gurumurthy ◽  
Felipe de Souza ◽  
Annesha Enam ◽  
Joshua Auld
Keyword(s):  
Autonomous Vehicles ◽  
High Performance ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Network ◽  
Supply And Demand ◽  
Transportation Systems ◽  
Point Of View ◽  
Computational Time ◽  
Vehicle Assignment

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

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