Diagnostic Analysis for an Autonomous Truck Using Multiple Attribute Decision Making

Gomes, Iago Pachêco; Bruno, Diego Renan; Osório, Fernando Santos; Wolf, Denis F.

doi:10.1109/lars/sbr/wre.2018.00059

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…This highlights how, by cutting transportation costs, ATs have the potential to drastically disrupt the present logistics sector. Gomes et al (2018) promote the use of multiple attribute decision-making and fuzzy sets to assist ATs in selecting safe navigation routes. To achieve consistent outcomes, the technique depends on the integration of perceptual systems and diagnostic data from an AT, even if a more comprehensive safety study is required.…”

Section: Literature Reviewmentioning

confidence: 99%

Life Cycle Employment and Carbon Footprint Assessment of Automation and Electrification in the US Trucking Sector

M. Mandouri,

C. Onat,

Kucukvar

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This study investigates the relationship of emissions reduction, technological transition, and employment dynamics within introducing autonomy to the trucking industry. Results highlight the urgency of adopting cleaner technologies and integrating autonomy to curtail greenhouse gas emissions. Despite additional emissions originating from manufacturing autonomous system components and additional maintenance operations, autonomous trucks offer fuel efficiency improvements and emission reductions compared to non-autonomous trucks. Employment implications reveal potential job losses due to automation overwhelming opportunities arising in autonomous system components manufacturing and maintenance, resulting in net losses of jobs within the industry. Transitioning from non-autonomous diesel trucks to autonomous battery electric trucks showcases a 50% reduction in emissions, highlighting the significance of combining autonomy and electrification in reducing emissions. The Green Employment Index is utilized to measure how green employment within the trucking industry is, unveiling a sustainable framework for job creation in tandem with emissions reduction. Based on this, for low penetration levels of less than 50%, transitioning from non-autonomous battery electric trucks to autonomous battery electric trucks offers the "greenest" employment opportunities.

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Section: Literature Reviewmentioning

confidence: 99%

Life Cycle Employment and Carbon Footprint Assessment of Automation and Electrification in the US Trucking Sector

M. Mandouri,

C. Onat,

Kucukvar

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…According to TOPSIS, the most preferred alternatives should have the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS) [24]. The TOPSIS method has found wide use in transportation and intelligent vehicle systems [11,25,26]. In [26], a hybrid approach was employed, combining the TOPSIS and AHP methods.…”

Section: Introductionmentioning

confidence: 99%

“…The TOPSIS method has found wide use in transportation and intelligent vehicle systems [11,25,26]. In [26], a hybrid approach was employed, combining the TOPSIS and AHP methods.…”

Section: Introductionmentioning

confidence: 99%

Safety System Assessment Case Study of Automated Vehicle Shuttle

et al. 2022

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Automated vehicle (AV) minibuses, i.e., AV shuttles, are gaining popularity in the testing of new types of transportation services in real traffic conditions. AV shuttles have moved from closed test areas to low-traffic public sites such as local residential areas, technology parks, university campuses, etc. These types of vehicles are usually low-speed and rely on a lidar-camera sensor set and a self-driving software stack. These new use cases are increasing these systems’ safety demands. In addition to functional safety, many other aspects need to be considered. In this study, a risk analysis model is developed, combining the fuzzy analytical hierarchy process and the Technique for Order of Preference by Similarity to Ideal Solution method. The proposed model is utilized to prioritize risks corresponding to the particular case study, based on real AV shuttle bus development, and focuses on the low-level hardware/software safety issues and improvements.

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A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles

et al. 2021

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Autonomous/self-driving vehicles have gained significant attention these days, as one of the intelligent transportation systems. However, those vehicles have risks related to their physical implementation and security against cyber threats. Therefore, this study proposes a new security-by-design model for estimating the uncertainty of autonomous vehicles and measuring cyber risks; thus it assists decision-makers in addressing the risks of the physical design and their attack surfaces. The proposed model is developed using neutrosophic sets that efficiently tackle multi-criteria decision-making (MCDM) problems with extensive conflicting criteria and alternatives. The proposed model integrates MCDM, Analytic Hierarchy Process (AHP), Multi-Attributive Border Approximation Area Comparison (MABAC), and Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II), along with single-valued neutrosophic sets (SVNSs). An illustrative case considering ten risks in self-driving vehicles is used to validate the feasibility of the proposed model. Compared to the state-of-the-art methods, the proposed model is considered consistent and reliable to deal with and represent uncertainty and incomplete risk information using neutrosophic sets.

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Diagnostic Analysis for an Autonomous Truck Using Multiple Attribute Decision Making

Cited by 6 publications

References 7 publications

Life Cycle Employment and Carbon Footprint Assessment of Automation and Electrification in the US Trucking Sector

Life Cycle Employment and Carbon Footprint Assessment of Automation and Electrification in the US Trucking Sector

Safety System Assessment Case Study of Automated Vehicle Shuttle

A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles

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