Smart City Gnosys

Smart city article details

Title	Deep Learning For Lidar-Based Autonomous Vehicles In Smart Cities
ID_Doc	17879
Authors	Ponnaganti V.; Moh M.; Moh T.-S.
Year	2021
Published	Handbook of Smart Cities
DOI	http://dx.doi.org/10.1007/978-3-030-69698-6_65
Abstract	Autonomous vehicles and deep learning are an integral part of smart cities. They interact and communicate with their surroundings, requiring high computer vision accuracy to maintain driver and pedestrian safety. Many autonomous vehicles leverage deep learning for detection and utilize a suite of sensors that are specific to their environment or use case. In such deep learning environment, sensor data is used as input to neural networks that make decisions regarding the vehicle's response or reaction to its environment. These sensors in autonomous vehicles provide details regarding the vehicle's surroundings and potential obstacles. Many sensor suites are starting to contain light detection and ranging (LiDAR) sensors, as the cost of the technology decreases and becomes more widely available. LiDAR technology uses focused light to detect distance, providing an accurate description of the sensor's surroundings, such precise account is crucial for autonomous driving in ever-changing smart city environments. This chapter covers different applications of LiDAR technology and the use of the sensor data in deep learning applications for smart cities. A case study is also featured to illustrate a potential implementation, which is followed by discussion of future research directions. © Springer Nature Switzerland AG 2021. All rights reserved.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
29114	0.882	Stahler J.; Markgraf C.; Pechinger M.; Gao D.W.	High-Performance Perception: A Camera-Based Approach For Smart Autonomous Electric Vehicles In Smart Cities	IEEE Electrification Magazine, 11, 2 (2023)
17826	0.876	Khalifa O.O.; Daud H.N.M.; Ali E.S.; Saeed M.M.	Deep Learning Approaches For Object Detection In Autonomous Driving: Smart Cities Perspective	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 587 LNICST (2025)
10091	0.875	He F.; Karami Olia P.; Jamili Oskouei R.; Hosseini M.; Peng Z.; Banirostam T.	Applications Of Deep Learning Techniques For Pedestrian Detection In Smart Environments: A Comprehensive Study	Journal of Advanced Transportation, 2021 (2021)
17959	0.874	Sudhakar K.; Lakshmi M.; Chaithra A.S.; Niveditha S.; Vadivel R.; Rubini P.; Selvameena R.	Deep Learning-Based Multisensor Fusion For Safe And Efficient Autonomous Vehicle Operations In Iot-Enabled Smart Cities	AI's role in enhanced automotive safety (2025)
19752	0.872	Abd Elhamied E.M.; Youssef S.M.	Development Of Smart 3D Object Detection Using Lidar Cloud-Data Collection	Journal of Physics: Conference Series, 2128, 1 (2021)
2560	0.862	Martinelli F.; Mercaldo F.; Santone A.	A Method For Finding Distance In Real-Time Car Detection Through Object Detection	Procedia Computer Science, 246, C (2024)
35171	0.861	Abbasi R.; Bashir A.K.; Alyamani H.J.; Amin F.; Doh J.; Chen J.	Lidar Point Cloud Compression, Processing And Learning For Autonomous Driving	IEEE Transactions on Intelligent Transportation Systems, 24, 1 (2023)
21764	0.857	Akiyama K.; Shinkuma R.; Yamamoto C.; Saito M.; Ito T.; Nihei K.; Iwai T.	Edge Computing System With Multi-Lidar Sensor Network For Robustness Of Autonomous Personal-Mobility	Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022 (2022)
18126	0.857	Vivekanand C.V.; Inbamalar T.M.; Amala Justus Selvam M.; Monika Devi A.; Mohanambikai A.; Pooja Shree K.	Deepdrive: Innovations In Distance Detection And Intervehicle Communication Through Deep Learning	4th International Conference on Communication, Computing and Industry 6.0, C216 2023 (2023)
23110	0.85	Guo D.; Moh M.; Moh T.-S.	End-To-End Learning For Autonomous Driving In Secured Smart Cities	Advanced Sciences and Technologies for Security Applications (2021)