Smart City Gnosys

Smart city article details

Title	Collision Prediction Of Smart Cities Using Co-Simulation
ID_Doc	14773
Authors	Karre A.; Azim A.
Year	2023
Published	Procedia Computer Science, 224
DOI	http://dx.doi.org/10.1016/j.procs.2023.09.029
Abstract	Road traffic has become prominent in everyday living, impacting or disrupting services to people and daily routines. With the rise in automobile manufacturing and the frequency of vehicle crashes, human catastrophes, like fatalities, accidents, impairments, and destruction of property, are surging yearly. Vehicle collision detection has recently gained prominence in decreasing manually operated and autonomous vehicle fatalities. The concept of independent and self-driving cars relies on accurate object recognition, including pedestrians, vehicles, buildings, and other moving objects. Various object-detecting approaches have been proposed to help autonomous vehicles (AVs) achieve consistent, safe driving. Object prediction and detection have noticed numerous algorithmic changes that have improved speed and accuracy. In this study, I used a traffic dataset produced by a CARLA simulator to anticipate collisions using the Yolov7 model. I generated the dataset from a CARLA simulation bench in video sequences, manually annotated the frames, and used the deep learning algorithm Yolov7 to train them. The model predicts the collision a few seconds before it occurs in real-time. I implemented this framework to increase the safety of driving in self-driving vehicles. © 2023 Elsevier B.V.. All rights reserved.
Author Keywords	Collision; Detection; Prediction; Safety; Simulator

Similar Articles

Id	Similarity	Authors	Title	Published
50560	0.884	Baiat Z.E.; Baydere S.	Smart City Traffic Monitoring:Yolov7 Transfer Learning Approach For Real-Time Vehicle Detection	2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 (2023)
35975	0.876	Singh B.; Kaunert C.; Chandra S.	Machine Learning For Safer Autonomous Vehicles: Tackling Traffic Detection And Collision In Smart Cities	Achieving Sustainability in Multi-Industry Settings With AI (2025)
9209	0.875	Reddy Konala T.; Nammi A.; Sree Tella D.	Analysis Of Live Video Object Detection Using Yolov5 And Yolov7	2023 4th International Conference for Emerging Technology, INCET 2023 (2023)
17826	0.873	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)
17876	0.873	Rajagopal M.; Sivasakthivel R.	Deep Learning For Intelligent Transportation: A Method To Detect Traffic Violation	AIP Conference Proceedings, 2763, 1 (2023)
44409	0.869	Alahdal N.M.; Abukhodair F.; Meftah L.H.; Cherif A.	Real-Time Object Detection In Autonomous Vehicles With Yolo	Procedia Computer Science, 246, C (2024)
35987	0.866	Singh B.; Kaunert C.	Machine Learning For Traffic Management, Object Detection, And Collision Avoidance In Autonomous Driving For Smart City Governance	Leveraging Futuristic Machine Learning and Next-Generational Security for e-Governance (2024)
29997	0.862	Ruzicka J.; Tichy T.; Hajciarova E.; Frydryn M.	Identification Of Collision Situations For Higher Efficiency Of Traffic Control System	2024 Smart Cities Symposium Prague, SCSP 2024 - Proceedings (2024)
62129	0.862	Deepthi shree A.M.; Brindha M.	Yolov8 For Robust Traffic Object Instance Segmentation Using Image Quality Assessment	Procedia Computer Science, 260 (2025)
26305	0.862	Singh B.	Featuring Smart City Solutions With Machine Learning For Traffic Management, Object Detection, And Collision Avoidance In Autonomous Driving	Urban Mobility and Challenges of Intelligent Transportation Systems (2025)