scholarly journals Employing waterborne autonomous vehicles for museum visits: a case study in Amsterdam

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Helena Hang Rong ◽  
Wei Tu ◽  
Fábio Duarte ◽  
Carlo Ratti
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Central Area ◽  
Urban Tourism ◽  
Innovative Approach ◽  
Multi Objective ◽  
Travel Experience ◽  
Points Of Interest ◽  
Objective Model

AbstractAmsterdam is a culturally rich city attracting millions of tourists. Popular activities in Amsterdam consist of museum visits and boat tours. By strategically combining them, this paper presents an innovative approach using waterborne autonomous vehicles (WAVs) to improve the museum visitation in Amsterdam. Multi-source urban data including I Amsterdam card data and Instagram hashtags are used to reveal museum characteristics such as offline and online popularity of museums and visitation patterns. A multi-objective model is proposed to optimize WAV routes by considering museum characteristics and travel experiences. An experiment in the Amsterdam Central area was conducted to evaluate the viability of employing WAVs. By comparing WAVs with land transportation, the results demonstrate that WAVs can enhance travel experience to cultural destinations. The presented innovative WAVs can be extended to a larger variety of points of interest in cities. These findings provide useful insights on embracing artificial intelligence in urban tourism.

scholarly journals Design and optimization of an RFID-enabled passport tracking system

2017 ◽  
Vol 5 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Abdulsalam Dukyil ◽  
Ahmed Mohammed ◽  
Mohamed Darwish
Keyword(s):  
Social Impact ◽  
Optimization Problem ◽  
Tracking System ◽  
Communication Performance ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Solution Methods ◽  
Objective Model ◽  
Effective Design

Abstract The implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of products as one of the most cutting edge technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially affected by interference between the RFID devices. It is also subject to additional costs in investment that should be taken into account. Consequently, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today's markets. This study presents a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, performance and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to real design in terms of considering the uncertain parameters, the developed multi-objective model was developed in terms of a fuzzy multi-objective model (FMOM). To solve the fuzzy multi-objective optimization problem, two solution methods were used and a decision-making method was employed to select the final trade-off solution. A case study was applied to examine the applicability of the developed model and the proposed solution methods. Highlights The problem is formulated as a fuzzy multi-objective programming model. Two solution methods are used to solve the optimization problem. A case study is investigated to examine the applicability of the model.

scholarly journals Novel Methods In Training Autonomous Vehicles For Urban Roads

2021 ◽  
Author(s):  
Rafael Vasquez
Keyword(s):  
Autonomous Vehicles ◽  
Control Policy ◽  
Negative Influence ◽  
Orientation Data ◽  
Passenger Comfort ◽  
Multi Objective ◽  
Human In The Loop ◽  
Preference Behaviour ◽  
Road Crossing

<div>This thesis presents the development and application of a novel platform to train autonomous vehicles (AV) for urban roads. Interactive and immersive virtual reality (VR) environments are developed for the collection of mobility preference, behaviour, movement, and orientation data. The resulting naturalistic data can be used directly to train AV control systems. This platform is exemplified in an end-to-end case study resulting in a multi-objective braking system which maximizes both pedestrian safety and passenger comfort. It begins with the development of an immersive VR pedestrian road-crossing environment and compilation of a unique, naturalistic dataset. A vehicle agent is then successfully trained against the dataset, learning a multi-objective brake control policy using deep reinforcement learning methods and reducing the negative influence on passenger comfort by half while maintaining safe braking operation. This platform offers the opportunity to simulate complex, human-in-the-loop scenarios AVs will inevitably face and train them for these scenarios.</div>

scholarly journals Application of Artificial Intelligence in Tesla- A Case Study

2021 ◽  
pp. 205-218
Author(s):  
Divya Kumari ◽  
Subrahmanya Bhat
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Financial Services ◽  
Autonomous Vehicles ◽  
Oil And Gas ◽  
Route Planning ◽  
Case Study Report ◽  
Deep Layers ◽  
Car Park

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth & Journey of the Tesla Inc. Company.

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