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An analysis of various deep learning-based target detection algorithms in the field of autonomous driving
2024
Applied and Computational Engineering
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Target detection is a crucial research objective within the domain of computer vision, finding extensive applications in areas such as robotics, autonomous driving, industrial inspections, and various ...
region) on automatic driving, compares the advantages and disadvantages of the two types of algorithms, as well as the results of detecting traffic signals, traffic vehicles, and pedestrians, and focuses ...
In traffic scenarios, the accuracy of traffic signal recognition is very important for autonomous driving systems. ...
doi:10.54254/2755-2721/42/20230670
fatcat:xsuuon6cfneyrd4dhz7ebygu3y
The application of deep learning in autonomous driving
2024
Applied and Computational Engineering
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Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. ...
Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion ...
Experiments show that the proposed method is robust to road traffic scenarios in complex environments. ...
doi:10.54254/2755-2721/50/20241379
fatcat:cuvwl2zejvbdxog4kug7zrne7a
Making Bertha See
2013
2013 IEEE International Conference on Computer Vision Workshops
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This paper presents details of the employed vision algorithms for object recognition and tracking, free-space analysis, traffic light recognition, lane recognition, as well as self-localization. ...
With the market introduction of the 2014 Mercedes-Benz S-Class vehicle equipped with a stereo camera system, autonomous driving has become a reality, at least in low speed highway scenarios. ...
From an algorithmic point-of-view, traffic light recognition involves three main problems: detection, classification and selection of the relevant light at complex intersections. ...
doi:10.1109/iccvw.2013.36
dblp:conf/iccvw/FrankePRKESH13
fatcat:7zb774kdtve2fds5y53t7tvtqu
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
[article]
2021
arXiv
pre-print
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Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation ...
systems in recent years. ...
In [67] , an autonomous driving simulation experiment is carried out through conditional imitation learning. In essence, the learning-based strategy is more suitable for complex dynamic scenarios. ...
arXiv:2106.13052v2
fatcat:6lfm4qd3ozetxmdl2rptbro6w4
AI in Autonomous Vehicles: A Comprehensive Study of Perception, Decision-Making, Path Planning, and Safety Considerations
2023
Zenodo
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Through an extensive review of research literature and case studies, this paper highlights the crucial role of AI in enabling safe and efficient autonomous driving. ...
This research paper provides a comprehensive study of AI applications in autonomous vehicles, focusing on perception, decision- making, path planning, and safety considerations. ...
Decision-Making Algorithms for Autonomous Vehicles: Decision-Making Frameworks in Autonomous Driving: Provides an overview of decision-making frameworks in autonomous vehicles.
II. ...
doi:10.5281/zenodo.8149709
fatcat:4fpcu6llczglroydqfgbwrtg3q
Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
2022
Applied Sciences
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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as ...
The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved ...
An improved traffic sign recognition algorithm was demonstrated by Cao et al. for an intelligent driving system [48] . ...
doi:10.3390/app12146831
fatcat:qkeylw67sngrtmmgwa2r3ue3ii
Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions
2022
Electronics
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In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. ...
In this paper, we have presented how You Only Look Once (YOLO)v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification ...
Object detection plays an essential role in developing smart cities in normal traffic conditions or autonomous environments. ...
doi:10.3390/electronics11040563
fatcat:hde5y6yorjat3p33fiuvpitw7u
Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends
2023
Sensors
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, obstacles, traffic signs, traffic lights, etc. ...
Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ...
., and the Satellite Communications and AIoT Research Center/The Co-operation Platform of the Industry-Academia Innovation School, National Yang Ming Chiao Tung University (NYCU), Taiwan R.OC. for their ...
doi:10.3390/s24010249
pmid:38203111
pmcid:PMC10781282
fatcat:pdpycubhlzcfhovi77bj5tv6ue
Autonomous Car
2019
International Journal for Research in Applied Science and Engineering Technology
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Car following and decision making are critical to complete driving mission for autonomous vehicles under complex and dynamic urban environment. ...
This paper describes the important parameters in the design of the autonomous car driving system. ...
Fig. 1 1 Pedestrian detection algorithm As in
Fig. 6 6 traffic sign recognition algorithm ...
doi:10.22214/ijraset.2019.4392
fatcat:jzpqtbufnrdq7iutyu67syj2sy
Neurosymbolic hybrid approach to driver collision warning
[article]
2022
arXiv
pre-print
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There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and ...
We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. ...
We would also like to thank Miami-Dade Police Department George Perera for providing valuable insight regarding the first response operation. ...
arXiv:2203.15076v1
fatcat:sibetc7zvjbk5ef4pwtfjlw5tq
A Brief Study on Lane Detection using Lane Boundary Marker Network
2023
International Journal of Advanced Research in Science, Communication and Technology
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We also demonstrate that our algorithm can be used in conjunction with other lane detectors to enhance their lane retrieval capabilities. ...
Both advanced driver assistance systems and self-driving automobiles rely heavily on lane detection. ...
Aspects of automation that we share with them the autonomous system is lane recognition. Lane recognition is essential for lane recognition and identification. Thorough driving safety. ...
doi:10.48175/ijarsct-7840
fatcat:6wmoke6qyjfpbaceudbcj2ijgm
Analysis and prospects of automobile intelligent assisted driving characteristics based on FPGA technology
2024
Applied and Computational Engineering
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With the ever-increasing demand for autonomous driving solutions, the adoption of FPGAs has become indispensable in meeting the requirements for high-speed data processing and instantaneous response times ...
These real-world examples serve to underscore the pivotal role that FPGAs play in ensuring road safety and propelling technological advancement in the realm of autonomous driving. ...
Image recognition algorithm Image recognition stands as a linchpin in autonomous driving systems, offering the ability to discern and classify objects in the vehicle's vicinity. ...
doi:10.54254/2755-2721/50/20241210
fatcat:4x44iqqrn5azzlbr3zspju3jdu
Application of deep learning in image processing of unmanned vehicles
2024
Applied and Computational Engineering
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Finally, we review the disadvantages of the application of deep learning in image processing for autonomous vehicles. ...
However, it is not easy to achieve full autonomy on account of the essence of a complex and dynamic driving environment. ...
It achieves an accuracy of 88.6% and is capable of improving accuracy by hyperparameter tuning and more data collection for adjusting to new scenarios [7] . ...
doi:10.54254/2755-2721/33/20230264
fatcat:4t4t2chgijgz7giwnzwqoup5iu
MFPE: A Loss Function based on Multi-task Autonomous Driving
2022
ECTI Transactions on Computer and Information Technology
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Road tracking, traffic sign recognition, obstacle avoidance, and real-time acceleration and deceleration are some critical sub-tasks in autonomous driving. ...
The experiments showed that the existing function combinations could not significantly improve the autonomous driving performance, and the loss function had a significant impact on the autonomous driving ...
ACKNOWLEDGMENT Youwei Li received scholarship support from CPALL for conducting this research in PIM. ...
doi:10.37936/ecti-cit.2022164.248304
fatcat:wm4c6qh34bbbngvv7ybi7u3g3q
Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario
[article]
2023
arXiv
pre-print
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However, further training is necessary to enable the AVP algorithm to adapt to complex scenarios and complete parking tasks in any given situation. ...
The autonomous valet parking (AVP) functionality in self-driving vehicles is currently capable of handling most simple parking tasks. ...
INTRODUCTION Simulation test scenarios are an important part of helping autonomous driving algorithms improve, but current simulation scenarios are still limited to manual approaches. ...
arXiv:2311.08410v1
fatcat:rz4qpov6d5enpdr5ezdieu65ra
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