Information Management Challenges in Autonomous Vehicles

Ghansiyal, Adrija; Mittal, Mamta; Kar, Arpan Kumar

doi:10.4018/jcit.20210701.oa5

Cited by 12 publications

(2 citation statements)

References 52 publications

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“…Data generation and Big Data management are other concerns. According to the Analysis of the author [102], cameras generate 20-40 MB of data per second, RADAR and SONAR generate 10-100 KB per second, GPS generates 50 KB per second, and Lidar generates 70-100 MB per second. Some of the research papers explored the difficulties that big data presents for IoV [103,104].…”

Section: F Information Managementmentioning

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: F Information Managementmentioning

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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show abstract

“…In VANETs, a variety of techniques are used for clustering [5] The routing and forwarding capabilities of vehicles alter with their position on urban highways because their positions frequently shift and are unevenly distributed. These issues cause the network to become unstable, necessitating the usage of a cluster model offers excellent stability for a dynamically changing VANET [6]. According to this investigation, most stability-related schemes are developed under the impact of a stability parameter.…”

Section: A Novel Approach For Dynamic Stable Clustering In Vanet Usin...mentioning

confidence: 99%

A Novel Approach for Dynamic Stable Clustering in VANET Using Deep Learning (LSTM) Model

Karne

Muralidharan

2022

IJEER

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Clustering in VANETs, which dynamically evolve into wireless networks, is difficult due to the networks' frequent disconnection and fast changing topology. The stability of the cluster head (CH) has a huge impact on the network's robustness and scalability. The overhead is decreased. The stable CH assures that intra- and inter-cluster communication is minimal. Because of these difficulties, the authors seek a CH selection technique based on a weighted combination of four variables: community neighborhood, quirkiness, befit factor, and trust. The stability of CH is influenced by the vehicle's speed, distance, velocity, and change in acceleration. These are considered for in the befit factor. Also, when changing the model, the precise location of the vehicle is critical. Thus, the predicted location is used to evaluate CH stability with the help of the Kalman filter. The results showed that the befit factor performed better than the latest developments. Because of the high speed of the vehicle, dynamic changes and frequent communication link breaks are unavoidable. In order to fully perceive issue, a graphing approach employed to assess the eccentricity then the communal neighborhood. Using Eigen gap heuristic, the link dependability is determined. Trust is the final important parameter that has not yet been taken into account in the weighted method. The trust levels are specifically being evaluated for the primary users using an adaptive spectrum sensing. Long short-term memory (LSTM), a deep recurrent learning network, used to train the likelihood of detection under diverse signal and noise situations. By using LSTM model, significantly decreased the false rate. The cluster head stability has improved for high traffic density, significantly improved according to the comparative analysis with the weighted and individual metrics. The efficiency of the network has also greatly increased in terms of throughput, packet delay, packet delay ratio, and energy consumption.

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User and Operator Friendly Outdoor Car Parking Lot Occupancy Detection (OCPLOD) System Design: Ondokuz Mayıs University Example

Aydın,

Genç,

Arslan

et al. 2024

TRANSBALTICA XIV: Transportation Science and Technology

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Information Management Challenges in Autonomous Vehicles

Cited by 12 publications

References 52 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

A Novel Approach for Dynamic Stable Clustering in VANET Using Deep Learning (LSTM) Model

User and Operator Friendly Outdoor Car Parking Lot Occupancy Detection (OCPLOD) System Design: Ondokuz Mayıs University Example

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