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Cited by 517Autonomous Vehicle Technology and SafetyVehicle Dynamics and Control SystemsHuman-Automation Interaction and Safety

A Review on Autonomous Vehicles: Progress, Methods and Challenges

Open access
Parekh, Darsh, Poddar, Nishi, Rajpurkar, Aakash, Chahal, Manisha, Kumar, Neeraj, Joshi, Gyanendra Prasad, Cho, Woong·2022·Electronics
DOI 10.3390/electronics11142162
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Abstract

Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

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  • Alternative strategies may be required in legislative frameworks that approve the use of AVs.

    “Furthermore, alternative strategies may be required to be used by legislative frameworks that approve the use of AVs [12][13][14].”

    From Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature (2024) · cites this paper as [14]

    The Research Questions Answers section says new regulations or legislation can set rules for how self-driving cars deal with foreseeable crashes, and the Conclusions identify governance as important to widespread AV use. These passages support considering new legislative approaches, but do not discuss frameworks that approve AV use or say those frameworks may require alternative strategies. No page numbers are available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient.

    “However, several studies have been published on the performance of ADS-mounted sensors, but research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient [16][17][18][19].”

    From Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic (2024) · cites this paper as [16]

    Section 3.1 defines ODD and relates it to an automated driving system’s operating conditions and safety responses. Section 6 discusses research gaps, but does not say that research measuring ODD’s impact on AV driving safety is insufficient. Searches for that specific gap found no passage addressing it. (LLM, conf=0.72)

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Research works printed but not linked: 41

  1. Zhang, T.; Zhang, L.; Zhao, L.; Huang, X.; Hou, Y. Catalytic Effects in the Cathode of Li-S Batteries: Accelerating polysulfides redox conversion. EnergyChem 2020, 2, 100036. [CrossRef] · DOI
  2. Li, W.; Guo, X.; Geng, P.; Du, M.; Jing, Q.; Chen, X.; Zhang, G.; Li, H.; Xu, Q.; Braunstein, P.; et al. Rational Design and General Synthesis of Multimetallic Metal-Organic Framework Nano-Octahedra for Enhanced Li-S Battery. Adv. Mater. 2021, 33, 2105163. [CrossRef] [PubMed] · DOI
  3. Geng, P.; Wang, L.; Du, M.; Bai, Y.; Li, W.; Liu, Y.; Chen, S.; Braunstein, P.; Xu, Q.; Pang, H. MIL-96-Al for Li-S Batteries: Shape or Size? Adv. Mater. 2021, 34, 2107836. [CrossRef] [PubMed] · DOI
  4. Zheng, S.; Li, Q.; Xue, H.; Pang, H.; Xu, Q. A highly alkaline-stable metal oxide@metal-organic framework composite for high-performance electrochemical energy storage. Natl. Sci. Rev. 2019, 7, 305-314. [CrossRef] [PubMed] · DOI
  5. Yaqoob, I.; Khan, L.U.; Kazmi, S.M.A.; Imran, M.; Guizani, N.; Hong, C.S. Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges. IEEE Netw. 2019, 34, 174-181. [CrossRef] · DOI
  6. Hussain, R.; Zeadally, S. Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Commun. Surv. Tutorials 2018, 21, 1275-1313. [CrossRef] 10. 10. · DOI · Paper page
  7. SAE International. Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicles J3016; SAE International: Warrendale, PA, USA, 2018; Volume J3016, p. 35. [CrossRef] · DOI
  8. Miglani, A.; Kumar, N. Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Veh. Commun. 2019, 20, 100184. [CrossRef] · DOI · Paper page
  9. Dai, Y.; Lee, S.-G. Perception, Planning and Control for Self-Driving System Based on On-board Sensors. Adv. Mech. Eng. 2020, 12, 1687814020956494. [CrossRef] · DOI
  10. Ahmed, M.; Seraj, R.; Islam, S.M.S. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics 2020, 9, 1295. [CrossRef] · DOI · Paper page
  11. Jain, V., Patnaik, S., Popent , iu Vlȃdicescu, F., Sethi, I., Eds; Advances in Intelligent Systems and Computing; Springer: Singapore, 2020; Volume 1006. [CrossRef] · DOI
  12. Zhao, J.; Liang, B.; Chen, Q. The key technology toward the self-driving car. Int. J. Intell. Unmanned Syst. 2018, 6, 2-20. [CrossRef] · DOI
  13. Gupta, A.; Anpalagan, A.; Guan, L.; Khwaja, A.S. Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array 2021, 10, 100057. [CrossRef] · DOI
  14. Jung, Y.; Seo, S.-W.; Kim, S.-W. Curb Detection and Tracking in Low-Resolution 3D Point Clouds Based on Optimization Framework. IEEE Trans. Intell. Transp. Syst. 2019, 21, 3893-3908. [CrossRef] · DOI
  15. Li, G.; Yang, Y.; Qu, X. Deep Learning Approaches on Pedestrian Detection in Hazy Weather. IEEE Trans. Ind. Electron. 2019, 67, 8889-8899. [CrossRef] · DOI
  16. Bachute, M.R.; Subhedar, J.M. Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms. Mach. Learn. Appl. 2021, 6, 100164. [CrossRef] · DOI
  17. Chen, G.; Mao, Z.; Yi, H.; Li, X.; Bai, B.; Liu, M.; Zhou, H. Pedestrian detection based on panoramic depth map transformed from 3d-lidar data. Period. Polytech. Electr. Eng. Comput. Sci. B 2020, 64, 274-285. [CrossRef] · DOI
  18. Bu, F.; Le, T.; Du, X.; Vasudevan, R.; Johnson-Roberson, M. Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images. IEEE Robot. Autom. Lett. 2019, 5, 1626-1633. [CrossRef] · DOI
  19. Zhang, S.; Yang, J.; Schiele, B. Occluded pedestrian detection through guided attention in cnns. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June 2018; pp. 6995-7003.
  20. Chiang, K.-W.; Tsai, G.-J.; Li, Y.-H.; Li, Y.; El-Sheimy, N. Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment. Remote Sens. 2020, 12, 1564. [CrossRef] · DOI
  21. Sommer, F.; Dürrwang, J.; Kriesten, R. Survey and Classification of Automotive Security Attacks. Information 2019, 10, 148. [CrossRef] · DOI
  22. Ring, M., Dürrwang, J., Sommer, F. and Kriesten, R. Survey on vehicular attacks-building a vulnerability database. In Proceedings of the 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Yokohama, Japan, 5-7 November 2015; pp. 208-212.
  23. Chowdhury, A.; Karmakar, G.; Kamruzzaman, J.; Jolfaei, A.; Das, R. Attacks on Self-Driving Cars and Their Countermeasures: A Survey. IEEE Access 2020, 8, 207308-207342. [CrossRef] · DOI
  24. Rana, M.M. IoT-based electric vehicle state estimation and control algorithms under cyber attacks. IEEE Internet Things J. 2019, 7, 874-881. [CrossRef] · DOI
  25. Liu, Q.; Mo, Y.; Mo, X.; Lv, C.; Mihankhah, E.; Wang, D. Secure pose estimation for autonomous vehicles under cyber attacks. In Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9-12 June 2019; pp. 1583-1588.
  26. Yin, G.; Li, J.; Jin, X.; Bian, C.; Chen, N. Integration of Motion Planning and Model-Predictive-Control-Based Control System for Autonomous Electric Vehicles. Transport 2015, 30, 353-360. [CrossRef] · DOI
  27. Dang, D.; Gao, F.; Hu, Q. Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling. Appl. Sci. 2020, 10, 3180. [CrossRef] · DOI
  28. Liu, S.; Zheng, K.; Zhao, L.; Fan, P. A driving intention prediction method based on hidden Markov model for autonomous driving. Comput. Commun. 2020, 157, 143-149. [CrossRef] · DOI
  29. García Cuenca, L.; Puertas, E.; Fernandez Andrés, J.; Aliane, N. Autonomous driving in roundabout maneuvers using reinforce- ment learning with Q-learning. Electronics 2019, 8, 1536. [CrossRef] · DOI
  30. Wu, X.; Qiao, B.; Su, C. Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change. Appl. Sci. 2020, 10, 1626. [CrossRef] · DOI
  31. Ortega, J.; Lengyel, H.; Szalay, Z. Overtaking maneuver scenario building for autonomous vehicles with PreScan software. Transp. Eng. 2020, 2, 100029. [CrossRef] · DOI
  32. Wasala, A.; Byrne, D.; Miesbauer, P.; O'Hanlon, J.; Heraty, P.; Barry, P. Trajectory based lateral control: A Reinforcement Learning case study. Eng. Appl. Artif. Intell. 2020, 94, 103799. [CrossRef] · DOI
  33. Gambino, A.; Sundar, S.S. Acceptance of self-driving cars: Does their posthuman ability make them more eerie or more desirable? In Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow Scotland, UK, 4-9 May 2019; pp. 1-6.
  34. Hong, J.W.; Wang, Y.; Lanz, P. Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. Int. J. Human-Computer Interact. 2020, 36, 1768-1774. [CrossRef] · DOI · Paper page
  35. Lee, J.D.; Kolodge, K. Exploring Trust in Self-Driving Vehicles Through Text Analysis. Hum. Factors: J. Hum. Factors Ergon. Soc. 2019, 62, 260-277. [CrossRef] [PubMed] · DOI
  36. Hong, J.W.; Cruz, I.; Williams, D. AI, you can drive my car: How we evaluate human drivers vs. self-driving cars. Comput. Hum. Behav. 2021, 125, 106944. [CrossRef] · DOI
  37. Kumar, N.; Rodrigues, J.J.P.C.; Chilamkurti, N. Bayesian Coalition Game as-a-Service for Content Distribution in Internet of Vehicles. IEEE Internet Things J. 2014, 1, 544-555. [CrossRef] · DOI
  38. Kumar, N.; Chilamkurti, N.; Park, J.H. ALCA: agent learning-based clustering algorithm in vehicular ad hoc networks. Pers. Ubiquitous Comput. 2012, 17, 1683-1692. [CrossRef] · DOI
  39. Lee, M.; Atkison, T. VANET applications: Past, present, and future. Veh. Commun. 2020, 28, 100310. doi: 10.1016/j.vehcom.2020.100310. [CrossRef] · DOI
  40. Kumar, N.; Kaur, K.; Misra, S.C.; Iqbal, R. An intelligent RFID-enabled authentication scheme for healthcare applications in vehicular mobile cloud. Peer-to-Peer Netw. Appl. 2015, 9, 824-840. [CrossRef] · DOI
  41. Kumar, N.; Misra, S.; Obaidat, M.S. Collaborative Learning Automata-Based Routing for Rescue Operations in Dense Urban Regions Using Vehicular Sensor Networks. IEEE Syst. J. 2014, 9, 1081-1090. [CrossRef · DOI

Cited by

Sorted by citations

Authors
Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G. P., & Cho, W.
Affiliations
  • Narsee Monjee Institute of Management Studies (IN)
  • Thapar Institute of Engineering & Technology (IN)
  • Sejong University (KR)
  • Daegu Catholic University (KR)
Corresponding author
Cho, Woong
ORCID
Venue
Electronics
Publisher
MDPI AG
Published
2022-07-11
Volume
11
Issue
14
Pages
2162
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics11142162
OpenAlex
W4285042965
Cited by
517
References
3
Citations by year
2026: 75 · 2025: 179 · 2024: 175 · 2023: 84 · 2022: 4
FWCI
30.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Electronics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Korea Agency for Infrastructure Technology Advancement (KAIA) (Grant 21AMDP-C161756-01)
  • Ministry of Land, Infrastructure and Transport (Grant 21AMDP-C161756-01)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
autonomous vehicles, autonomous driving, pedestrian detection, path planning, motion control, automotive cybersecurity, vehicle localization, intelligent transportation systems

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