Major Limitations and Concerns Regarding the Integration of Autonomous Vehicles in Urban Transportation Systems

Fafoutellis, Panagiotis; Mantouka, Eleni G.

doi:10.1007/978-3-030-02305-8_89

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…The car's artificial intelligence algorithms for navigation and environment recognition still struggle to work appropriately in crowded urban areas. [85]. These algorithms rely on various sensors, such as cameras, radars, and Lidars, to perceive the surrounding environment and make decisions.…”

Section: Technological Efficiencymentioning

confidence: 99%

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Chougule,

Chamola,

Sam

et al. 2024

IEEE Open J. Veh. Technol.

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The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, selfdriving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.

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Section: Technological Efficiencymentioning

confidence: 99%

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Chougule,

Chamola,

Sam

et al. 2024

IEEE Open J. Veh. Technol.

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“…The Basic Stanley (BS) controller is divided into three regions, as illustrated in equation (1). These consist of the (low-high saturated -nominal) regions.…”

Section: Basic Stanleymentioning

confidence: 99%

“…By the end of 2025, self-driving cars are expected to be technologically mature enough for widespread use. 1 The most significant benefits of integrating autonomous vehicles into the transportation system are: (1) AVs are not prone to human errors, which will ensure passengers' safety during the trip, (2) Allows more significant opportunity to utilize wasted driving time in increasing production, (3) Providing simple, safe, and private transportation for those with disabilities that prohibit them from driving, and (4) Increase road capacity and reduce congestion, resulting in a saving of wasted energy ''fuel consumption.'' 2 In order for any self-driving car to be able to plan, navigate, and perform tasks in its environment, it has to be able to localize itself in this environment as the vehicle position is the direct feedback of any lateral controller.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy predictive Stanley lateral controller with adaptive prediction horizon

Abdelmoniem

Ali

Taher

et al. 2023

Measurement and Control

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The challenge of trajectory tracking of autonomous vehicles (AVs) is a critical aspect that must be effectively addressed. Recent studies are concerned with maintaining the yaw stability to guarantee the customers’ comfort throughout the journey. Most of the geometrical controllers solve this task by dividing it into consecutive point stabilization problems, limiting the controllers’ ability to handle sudden trajectory changes. One research presented a predictive Stanley lateral controller that uses a discrete prediction model to mimic human behavior by anticipating the vehicle’s future states. That controller is limited in its use, as the parameters must be manually tuned for every change in the maneuver or vehicle velocity. This article presents a novel approach for trajectory tracking in autonomous vehicles, by introducing a fuzzy supervisory controller that automatically adapts to changes in the vehicle’s velocity and maneuver by estimating the prediction horizon’s length and providing different weights for each controller. The proposed method overcomes the limitations of traditional controllers that require manual tuning of parameters, making it ready for real-world experiments. This is the main contribution of the research in this paper. The suggested technique demonstrated an advantage over the Basic Stanley controller and the manually tuned predictive Stanley controller in terms of the total lateral error and the model predictive control (MPC) in terms of computational time. The performance is determined by performing various simulations on V-Rep and hardware-in-the-loop (HIL) experiments on an E-CAR golf bus. A broad selection of velocities is used to validate the behavior of the vehicle while working on different maneuvers (double lane change, hook road, S road, and curved road).

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“…But the tort liabilities (is the car owner or the manufacturer liable for accidents) as well as data privacy in highly connected cars are still under consideration. Literature discusses this extensively, see [44][45][46][47]. When following the infrastructure-based automation concept, pursued by MAUDE, an additional third party next to the manufacturer and the car owner enters: the infrastructure provider.…”

Section: Legislative Considerations Of the Vehicle Conceptmentioning

confidence: 99%

New Operating Strategies for an On-the-Road Modular, Electric and Autonomous Vehicle Concept in Urban Transportation

Ulrich

Friedrich

Weimer

et al. 2019

WEVJ

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Today commercial transport in urban areas faces major challenges. These include making optimal use of limited space, avoiding empty trips, meeting driver shortages as well as reducing costs and emissions such as CO 2 , particulate matter and noise. The mutual acceleration and reinforcement of technological trends such as electrification, digitization and automation may enable new vehicle and mobility concepts that can meet these challenges. One possible vehicle concept is presented in this article. It is based on on-the-road modularization, i.e., a vehicle that can change different transport capsules during operation. The vehicle is divided into an electrically propelled autonomous drive unit and a transport unit. Standardized interfaces between these units enable the easy design of capsules for different uses, while the drive unit can be used universally. Business models and operating strategies that allow optimal use of this vehicle concept are discussed in depth in the article. First, the current situation is analyzed followed by a detailed description of an exemplary business model using a business model canvas. The operating strategies and logistics concepts are illustrated and compared with conventional concepts.

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Major Limitations and Concerns Regarding the Integration of Autonomous Vehicles in Urban Transportation Systems

Cited by 8 publications

References 19 publications

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Fuzzy predictive Stanley lateral controller with adaptive prediction horizon

New Operating Strategies for an On-the-Road Modular, Electric and Autonomous Vehicle Concept in Urban Transportation

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