Users, planners, and governments perspectives: A public survey on autonomous vehicles future advancements

Alsalman, Ali; Assi, Lateef; Ghotbi, Shabnam; Ghahari, SeyedAli; Shubbar, Ali A.

doi:10.1016/j.treng.2020.100044

Cited by 23 publications

(10 citation statements)

References 20 publications

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“…Currently, level 2 cars are available on the market. Level 3 cars (i.e., the driver needs to take control in special circumstances, although most of the actions are performed by the vehicle) are available to consumers at a limited number and option-only Audi A8 has the level 3 feature [30]. Level 4 and level 5 cars are anticipated to hit the market in the near future.…”

Section: Calibration and Validation Of Modelmentioning

confidence: 99%

Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School

Saha

Fatmi

2021

Sustainability

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This paper presents how a post-secondary institution like University of British Columbia’s Okanagan (UBCO) campus can reduce its carbon footprint and be aligned with the government’s target through promoting virtual campus and autonomous electric vehicles (AEVs). Different virtual campus scenarios are developed: online classes only, working-from-home only, and a hybrid of both. In the case of AEVs, alternative penetration rates for levels 2 and 5 are considered. A total of 50 scenarios are tested using a sub-area transport simulation model for UBCO, which is extracted from the regional travel demand forecasting model. The results suggest that a 40% AEV penetration rate coupled with fully in-person classes reduces GHG by ~36% compared to the 2018-level, which will help UBCO to achieve their 2030 emission reduction target and be aligned with the provincial target. The 50% AEV and 10% hybrid virtual campus reduces emissions by ~48%, which is aligned with the 2040 provincial target. A fully virtual campus will help to reach the 2050 provincial target by reducing GHG by ~76%. The results further demonstrate that level 5 AEVs produce lesser emissions than level 2 at a lower AEV penetration rate for the fully in-person campus scenario. At higher penetration rates, level 5 performs better only if it is coupled with 10% of students, faculties and staffs attending virtual campus scenario.

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Section: Calibration and Validation Of Modelmentioning

confidence: 99%

Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School

Saha

Fatmi

2021

Sustainability

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“…The imminent challenges relevant to the implementation and utilization of autonomous mobility (AM) in daily life remain a global concern, raising interest from industrial and academic experts to address specific uncertainties. Autonomous vehicles (AVs) gained momentum in the research and adoption process, as they are advantageous over conventional vehicles in aspects of energy efficiency [1,2], reduction in emissions from driving toward a climate-neutral environment [3][4][5], and the declension of road fatalities [6][7][8]. The progress toward autonomous mobility in a global perspective is considered achievable by 2050.…”

Section: Introductionmentioning

confidence: 99%

A National Innovation System Concept-Based Analysis of Autonomous Vehicles’ Potential in Reaching Zero-Emission Fleets

Venkatesh,

Raslavičius

2024

Technologies

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Change management for technology adoption in the transportation sector is often used to address long-term challenges characterized by complexity, uncertainty, and ambiguity. Especially when technology is still evolving, an analysis of these challenges can help explore different alternative future pathways. Therefore, the analysis of development trajectories, correlations between key system variables, and the rate of change within the entire road transportation system can guide action toward sustainability. By adopting the National Innovation System concept, we evaluated the possibilities of an autonomous vehicle option to reach a zero-emission fleet. A case-specific analysis was conducted to evaluate the industry capacities, performance of R&D organizations, main objectives of future market-oriented reforms in the power sector, policy implications, and other aspects to gain insightful perspectives. Environmental insights for transportation sector scenarios in 2021, 2030, and 2050 were explored and analyzed using the COPERT v5.5.1 software program. This study offers a new perspective for road transport decarbonization research and adds new insights to the obtained correlation between the NIS dynamics and achievement of sustainability goals. In 2050, it is expected to achieve 100% carbon neutrality in the PC segment and ~85% in the HDV segment. Finally, four broad conclusions emerged from this research as a consequence of the analysis.

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“…These self-driving cars, an epitome of cutting-edge implementation, incorporate many intelligent transport systems technologies within innovative urban transport systems (Bala et al, 2022). Equipped with advanced sensors and algorithms, autonomous vehicles can navigate and operate autonomously, eliminating the need for human intervention (Alsalman et al, 2021). They have significant potential for enhancing road safety, mitigating traffic congestion, and providing sustainable mobility solutions for future cities (Alsalman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Khan,

Raza,

Abbas

et al. 2024

Front. Built Environ.

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Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

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