How to Cite
Abstract
Because of urbanization and the increasing number of vehicles on the road, traffic has become more complex, leading to increasing car accidents and traffic congestion. Self-driving car technology research has made things easier on the road and change the traditional transportation way. Lots of research work focuses on the perception of transportation environment, path planning, etc. However, fundamental issues like accurate positioning, map building of the environment, etc., still need more work to assure safety and reliability of self-driving cars Accurate positioning and mapping are important for self-driving cars because both of them are the necessary components to enhance the safety and trustworthiness of self-driving cars. In the real world, GPS is not one hundred percent accurate and there are no GPS signals in tunnels and urban canyons. Furthermore, lanes, buildings, and trees can reduce the accuracy of GNSS greatly.
References
J. Fayyad, M. A. Jaradat, D. Gruyer, and H. Najjaran, "Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review," 2020. ncbi.nlm.nih.gov
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A Survey of Deep Learning Techniques for Autonomous Driving," 2019. [PDF]
R. Sadli, M. Afkir, A. Hadid, A. Rivenq et al., "Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy †," 2022. ncbi.nlm.nih.gov
B. Udugama, "Evolution of SLAM: Toward the Robust-Perception of Autonomy," 2023. [PDF]
Shaik, Mahammad, et al. "Enhancing User Privacy in Decentralized Identity Management: A Comparative Analysis of Zero-Knowledge Proofs and Anonymization Techniques on Blockchain Infrastructures." Journal of Science & Technology1.1 (2020): 193-218.
Tatineni, Sumanth. "Enhancing Fraud Detection in Financial Transactions using Machine Learning and Blockchain." International Journal of Information Technology and Management Information Systems (IJITMIS) 11.1 (2020): 8-15.
Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
Tatineni, Sumanth. "Deep Learning for Natural Language Processing in Low-Resource Languages." International Journal of Advanced Research in Engineering and Technology (IJARET) 11.5 (2020): 1301-1311.
S. Kuutti, R. Bowden, Y. Jin, P. Barber et al., "A Survey of Deep Learning Applications to Autonomous Vehicle Control," 2019. [PDF]
M. Rahimi, H. Liu, I. Durazo Cardenas, A. Starr et al., "A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems," 2022. ncbi.nlm.nih.gov
V. Vaquero, K. Fischer, F. Moreno-Noguer, A. Sanfeliu et al., "Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds," 2019. [PDF]
C. R. Salas Guerra, "Epistemological approach in immersive virtual environments and the neurophysiology learning process," 2019. [PDF]
N. Lopac, I. Jurdana, A. Brnelić, and T. Krljan, "Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector," 2022. ncbi.nlm.nih.gov
K. Nickels, J. Gassaway, M. Bries, D. Anthony et al., "Persistent Mapping of Sensor Data for Medium-Term Autonomy," 2022. ncbi.nlm.nih.gov
L. Caltagirone, M. Bellone, L. Svensson, M. Wahde et al., "Lidar–Camera Semi-Supervised Learning for Semantic Segmentation," 2021. ncbi.nlm.nih.gov
Y. Wang, Z. Feng, L. Song, X. Liu et al., "Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning," 2021. ncbi.nlm.nih.gov
M. Valente, C. Joly, and A. de La Fortelle, "An LSTM Network for Real-Time Odometry Estimation," 2019. [PDF]
H. Tibebu, V. De-Silva, C. Artaud, R. Pina et al., "Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation," 2022. ncbi.nlm.nih.gov
T. Gomes, D. Matias, A. Campos, L. Cunha et al., "A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors," 2023. ncbi.nlm.nih.gov
A. Asgharpoor Golroudbari and M. Hossein Sabour, "Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review," 2023. [PDF]
J. Laconte, A. Kasmi, R. Aufrère, M. Vaidis et al., "A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios," 2021. ncbi.nlm.nih.gov