Advanced Data Science Applications in Vehicles: A Comprehensive Review
DOI:
https://doi.org/10.47604/ijts.2402Keywords:
Data Science Applications, Vehicular Systems, Edge Computing, Machine Learning Algorithms, Autonomous DrivingAbstract
Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.
Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.
Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.
Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.
Downloads
References
Autowiseai. (2021). What data do autonomous vehicles collect? Proven technology Guaranteed performance.https://www.linkedin.com/pulse/what-data-do-autonomous-vehicles-collect-autowiseai
Henry, A., Hesham-El-Sayed & Manzoor, K. (2021). An overview of sensors in Autonomous Vehicles. International Workshop on Smart Communication and Autonomous Driving, 198(2022), 736-741. https://doi.org/10.1016/j.procs.2021.12.315
Ghaffarpasand, O., Burke, M., Louisa, K.,Ursell, H.,Chapman, S & Francis D, P. (2022). Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review. MDPI. Sustainable Transportation, 14(24), 1-20. https://doi.org/10.3390/su142416386
Shaoshan, L.,Liangkai, L., Jie, T., Bo, Y., Yifan, W, & Weisong, S. (2019). Edge Computing for Autonomous Driving: Opportunities and Challenges. Proceedings of the IEEE, 107(8), 1697-1716. https://doi 10.1109/jproc.2019.2915983
Yatin, S. (2023). Use Cases Of Machine Learning In The Automotive Industry. Machine Learning.https://hashstudioz.com/blog/use-cases-of-machine-learning-in-the-automotive-industry/
Drishti, S. (2022). Application of AI in Predictive Maintenance of Vehicles. https://www.analyticsvidhya.com/blog/2022/07/application-of-ai-in-predictive-maintenance-of-vehicles/
Vasavi, S., Aswarth, K., Durga ,S.,Gokhale, A. (2021). Predictive analytics as a service for vehicle health monitoring using edge computing and AK-NN algorithm. Materials Today: Proceedings, 46(17), 8645-8654. https://www.sciencedirect.com/science/article/abs/pii/S2214785321027553
Jonathan Labrie (2024). Impact of AI & Edge AI Technology On The Automotive Industry. https://www.linkedin.com/pulse/impact-ai-edge-technology-automotive-industry-jonathan-labrie utyyc#:~:text=Optimizing%20Traffic%20Management%20and%20Navigation&text=Additionally%2C%20Edge%20AI%20enables%20real,areas%20with%20poor%20network%20coverage.
Gómez, L. (2017). A.Data science applications to connected vehicles. Joint Research Centre (European Commission), 2-34. doi:10.2760/822136
Priyadarshan, P. (2022). Applications of Deep Learning in Traffic Management: A Review. International Journal of Business Intelligence and Big Data Analytics, 5(1), 16-23. https://research.tensorgate.org/index.php/IJBIBDA/article/view/26
Abalta.(2018).Autonomous vehicles: The data problem. https://abaltatech.com/blog/2018/06/autonomous-data-problem/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hirya Richard Edymond
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.