AI-Based Enhancements for Autonomous Vehicle Sensor Fusion
PDF

Keywords

Autonomous
Vehicle Sensor Fusion

How to Cite

[1]
D. N. Popova, “AI-Based Enhancements for Autonomous Vehicle Sensor Fusion”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 2, pp. 80–96, Oct. 2024, Accessed: Nov. 14, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/117

Abstract

Enhanced perception and navigation are the focus of the constant innovations in sensor technologies for autonomous vehicles. In the modern perception-led definition of vehicle automation, precise and accurate fusion of all the different sensor modalities, so-called sensor fusion, is crucial. Despite their substantial progress, most sensors have inherent limitations. These limitations necessitate the triggering of different sensors in different operating conditions or the fusion of redundant sensors, which can lead to significant improvements in the satisfaction of the automation functional and system requirements while ensuring fail-safe operation.

PDF

References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Pal, Dheeraj Kumar Dukhiram, et al. "AIOps: Integrating AI and Machine Learning into IT Operations." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 288-311.

Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.

J. Singh, “Robust AI Algorithms for Autonomous Vehicle Perception: Fusing Sensor Data from Vision, LiDAR, and Radar for Enhanced Safety”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 118–157, Apr. 2024

Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.

Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.

Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.

Tamanampudi, Venkata Mohit. "AI-Powered NLP Agents in DevOps: Automating Log Analysis, Event Correlation, and Incident Response in Large-Scale Enterprise Systems." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 646-689.

J. Singh, “The Ethical Implications of AI and RAG Models in Content Generation: Bias, Misinformation, and Privacy Concerns”, J. Sci. Tech., vol. 4, no. 1, pp. 156–170, Feb. 2023

S. Kumari, “Optimizing Mobile Platform Security with AI-Powered Real-Time Threat Intelligence: A Study on Leveraging Machine Learning for Enhancing Mobile Cybersecurity”, J. of Art. Int. Research, vol. 4, no. 1, pp. 332–355, Jan. 2024.

Praveen, S. Phani, et al. "Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management." Journal of Intelligent Systems & Internet of Things 13.1 (2024).

Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.

Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.

Downloads

Download data is not yet available.