scholarly journals A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 384
Author(s):  
Mohammad Reza Jabbarpour ◽  
Ali Mohammad Saghiri ◽  
Mehdi Sookhak
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Selection Process ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Component Selection ◽  
Wide Range ◽  
Selection Framework

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

scholarly journals Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yu-Cheng Fan ◽  
Sheng-Bi Wang
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Three Dimensional ◽  
Original Data ◽  
Depth Information ◽  
Learning Technology ◽  
Cloud Data ◽  
Self Driving Cars

With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals Disruptive Impacts of Automated Driving Systems on the Built Environment and Land Use: An Urban Planner’s Perspective

2019 ◽  
Vol 5 (2) ◽  
pp. 24 ◽  
Author(s):  
Yigitcanlar ◽  
Wilson ◽  
Kamruzzaman
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Capital Investment ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Urban Transport ◽  
Current Status ◽  
Smart Mobility

Cities have started to restructure themselves into ‘smart cities’ to address the challenges of the 21st Century—such as climate change, sustainable development, and digital disruption. One of the major obstacles to success for a smart city is to tackle the mobility and accessibility issues via ‘smart mobility’ solutions. At the verge of the age of smart urbanism, autonomous vehicle technology is seen as an opportunity to realize the smart mobility vision of cities. However, this innovative technological advancement is also speculated to bring a major disruption in urban transport, land use, employment, parking, car ownership, infrastructure design, capital investment decisions, sustainability, mobility, and traffic safety. Despite the potential threats, urban planners and managers are not yet prepared to develop autonomous vehicle strategies for cities to deal with these threats. This is mainly due to a lack of knowledge on the social implications of autonomous capabilities and how exactly they will disrupt our cities. This viewpoint provides a snapshot of the current status of vehicle automation, the direction in which the field is moving forward, the potential impacts of systematic adoption of autonomous vehicles, and how urban planners can mitigate the built environment and land use disruption of autonomous vehicles.

scholarly journals SAGE: A S plit- A rchitecture Methodolo g y for E fficient End-to-End Autonomous Vehicle Control

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-22
Author(s):  
Arnav Malawade ◽  
Mohanad Odema ◽  
Sebastien Lajeunesse-degroot ◽  
Mohammad Abdullah Al Faruque
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Industry Standard ◽  
Wide Range ◽  
Energy Consuming ◽  
Time Latency ◽  
4G Lte ◽  
Hardware Platforms

Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles’ driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge, energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model’s performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge, devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies. Compared to edge-only computation, SAGE reduces energy consumption by an average of 36.13% , 47.07% , and 55.66% for an AV with one low-resolution camera, one high-resolution camera, and three high-resolution cameras, respectively. SAGE also reduces upload data size by up to 98.40% compared to direct camera offloading.

scholarly journals CO-CREATION WORKSHOP FOR INTERFACE DESIGN - DESIGNING INNOVATIVE HMI FOR AUTOMATED VEHICLES

2019 ◽  
Vol 7 (Especial) ◽  
pp. 24
Author(s):  
Manuela Quaresma ◽  
Isabela Motta
Keyword(s):  
Interface Design ◽  
Autonomous Vehicles ◽  
New Technology ◽  
Information Design ◽  
Innovative Product ◽  
Human Machine Interfaces ◽  
Wide Range ◽  
Design Students ◽  
Group Brainstorming

 The increasing growth in the development of new technology is changing the way users interact with products. Thus, designers must re-think the human-machine interfaces (HMI) for communicating information regularly. Co-creation – the process of joint creation of users and designers – is an approach to product and experience development that can substantially benefit the generation of innovative product proposals. The present paper aims to assess the feasibility of co-creation workshops for the development of proposals for interfaces of Artificial Intelligence derived products through a case study on interfaces for autonomous vehicles. For this purpose, two sessions of co-creation workshops using group brainstorming techniques were conducted with undergraduate and graduate Design students and professors, who are specialists in interaction and information design. The results showed that the interfaces proposed by the designers were communicated information in a wide range of ways, making use of existing technologies to present messages in an innovative manner, meeting autonomous vehicles’ users’ needs.

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.

Components for Composite Material Customization System

2009 ◽  
Author(s):  
Soumitra Nandi ◽  
Zahed Siddique
Keyword(s):  
Composite Materials ◽  
Material Selection ◽  
Selection Process ◽  
Material Design ◽  
Optimization Techniques ◽  
Conventional Approach ◽  
Wide Range ◽  
Properties Of Materials ◽  
Selection Of

With the advancements of composite materials and research in nano-composites, designers have the flexibility to select materials from a wide range of properties to meet their specific design needs. Even with all these advancements, the material selection process during design follows a very conventional approach. The conventional approach to material design is to select a certain material from a given pre-set material list that allows the attainment of nearest properties required for the product. One of the disadvantages of this approach is that the trade-off inherent in the selection of material, when multiple properties are targeted, can be cumbersome to achieve or addressed at all. In this paper we present an approach to select and design composite materials, where the designer will have flexibility to select multiple properties of materials during the design of a new product. This approach employs an index for selection combined with heuristic optimization techniques to select the optimized combination of composite materials that could meet closest possible property goals. In the case study presented in this paper, we did not perform any optimization; rather, emphasize is given to the explanation of material selection technique, and an RMS value is introduced as an index for the selection.

Integration of GPS and Accelerometer Uncertainties to Improve the Estimation of the Pose of Autonomous Vehicles

2010 ◽  
Author(s):  
Wilson O. Achicanoy M. ◽  
Carlos F. Rodriguez H.
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Sampling Rate ◽  
System A ◽  
Accelerometer Signal ◽  
Gps Measurement ◽  
Global Positioning ◽  
The Individual ◽  
Uncertainty Models

Uncertainty fusion techniques based on Kalman filtering are commonly used to provide a better estimation of the state of a system. A comparison between three different methods to combine the sensor information in order to improve the estimation of the pose of an autonomous vehicle is presented. Two sensors and their uncertainty models are used to measure the observables states of a process: a Global Positioning System (GPS) and an accelerometer. Given that GPS has low sampling rate and the uncertainty of the position, calculated by double integration from the accelerometer signal, increases with time, first a resetting of the estimator based on accelerometer by the GPS measurement is done. Next, a second method makes the fusion of both sensor uncertainties to calculate the estimation. Finally, a double estimation is done, one for each sensor, and a estimated state is calculated joining the individual estimations. These methods are explained by a case study of a guided bomb.

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