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Road vehicle recognition algorithm in safety assistant driving based on artificial intelligence

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Abstract

Transportation has always been an indispensable part of people’s lives. With the growth of the total population, the number of trips continues to increase, traffic pressure continues to increase, and road traffic accidents are increasing every year. With the continuous development of artificial intelligence technology, the emergence of intelligent transportation systems has greatly promoted the development and commercialization of irreversible intelligent transportation systems. This paper aims to study the road vehicle recognition algorithm in safety-assisted driving based on artificial intelligence. The research topic of this paper is the research of road vehicle recognition algorithm in safety-assisted driving. The research focus is on lane detection algorithm and vehicle detection algorithm. The concept of lane line detection is proposed, and the image preprocessing method is introduced. The experimental results in this paper show that when the road environment is complex, the vehicle itself will cause misjudgments, but the overall correct rate is over 92.5%.

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References

  • Ahn D, Shin S, Park K et al (2018) Functional safety concept design and verification for longitudinal driving assistance system of an autonomous vehicle. Trans Korean Soc Automot Eng 26(2):149–158

    Article  Google Scholar 

  • Anders J, Mefenza M, Bobda C et al (2016) A hardware/software prototyping system for driving assistance investigations. J Real-Time Image Proc 11(3):559–569

    Article  Google Scholar 

  • Appathurai A, Sundarasekar R, Raja C et al (2020) An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits SystSignal Process 39(2):734–756

    Article  Google Scholar 

  • Barua A, Kalwa J, Shardt Y et al (2018) Path planning for an identification mission of an autonomous underwater vehicle in a lemniscate form. IFAC-PapersOnLine 51(29):323–328

    Article  Google Scholar 

  • Braunagel C, Rosenstiel W, Kasneci E (2017) Ready for take-over? a new driver assistance system for an automated classification of driver take-over readiness. IEEE Intell Transp Syst Mag 9(4):10–22

    Article  Google Scholar 

  • Chauhan S, Srivastava V (2017) Matlab based vehicle number plate recognition. Int J Comput Intell Res 13(9):2283–2288

    Google Scholar 

  • Chen CH (2020) A cell probe-based method for vehicle speed estimation. IEICE Trans Fundam Electron Commun Comput Sci 103:265–267

    Article  Google Scholar 

  • Dutta AK, Elhoseny M, Dahiya V, Shankar K (2019) An efficient hierarchical clustering protocol for multihop Internet of vehicles communication. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.3690

    Article  Google Scholar 

  • Hadavi M, Shafahi Y (2016) Vehicle identification sensor models for origin–destination estimation. Transp Res Part B Methodol 89:82–106

    Article  Google Scholar 

  • Hou J, Zeng H, Cai L et al (2019) Multi-label learning with multi-label smoothing regularization for vehicle re-identification. Neurocomputing 345:15–22

    Article  Google Scholar 

  • Kurihashi S, Kanaka K (2016) Mutual assistance system for automobile safety. Ifac Papersonline 49(19):438–443

    Article  Google Scholar 

  • Li M, Cao H, Song X et al (2018a) Shared control driver assistance system based on driving intention and situation assessment. IEEE Trans Industr Inf 14(11):4982–4994

    Article  Google Scholar 

  • Li R, Liu Z, Zhang R (2018) Studying the benefits of carpooling in an urban area using automatic vehicle identification data. Transp Res Part C: Emerg Technol 93:367–380

    Article  Google Scholar 

  • Liu Z, Kircher K (2017) Comparison of a time- and a speed-based traffic light assistance system. Cogn Technol Work 20(3):1–11

    Google Scholar 

  • Liu Y, Wang S, Fu X et al (2019) A network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane. Aust Environ Plan 51(2):279–282

    Article  Google Scholar 

  • Ma C, Gong H, Liu N et al (2017) DNAS: a driver nighttime assistance system using rear-view smartphone. Int J Ad Hoc Ubiquitous Comput 26(2):104–114

    Article  Google Scholar 

  • Maier O, Pfeiffer M, Wrede J (2016) Development of a braking dynamics assistance system for electric bicycles: design, implementation and evaluation of road tests. IEEE/ASME Trans Mechatron 21(3):1671–1679

    Article  Google Scholar 

  • Mo C, Li Y, Zheng L (2018) Simulation and analysis on overtaking safety assistance system based on vehicle-to-vehicle communication. Automot Innov 1(2):158–166

    Article  Google Scholar 

  • Pilataxi JI, Robalino W, Garcia DC (2016) Design and implementation of a driving assistance system in a car-like robot when fatigue in the user is detected. IEEE Lat Am Trans 14(2):457–462

    Article  Google Scholar 

  • Preuk K, Stemmler E, Schiessl C et al (2016) Does assisted driving behavior lead to safety-critical encounters with unequipped vehicles’ drivers? Accid Anal Prev 95:149–156

    Article  Google Scholar 

  • Rongjie Y, Adel-Aty M et al (2016) Multi-level Bayesian safety analysis with unprocessed automatic vehicle identification data for an urban expressway. Accid Anal Prev 88:68–76

    Article  Google Scholar 

  • Saito Y, Yoshimi R, Kume S et al (2021) Effects of a driver assistance system with foresighted deceleration control on the driving performance of elderly and younger drivers. Transp Res F Traffic Psychol Behav 77(1):221–235

    Article  Google Scholar 

  • Schoemig N, Heckmann M, Wersing H et al (2018) Please watch right’ - evaluation of a speech-based on-demand assistance system for urban intersections. Transp Res 54:196–210

    Google Scholar 

  • Shi Q, Abdel-Aty M, Yu R (2016) Multi-level Bayesian safety analysis with unprocessed automatic vehicle identification data for an urban expressway. Accid Anal Prev 88:68–76

    Article  Google Scholar 

  • Statter M, Strickland J, Quinlan K et al (2016) The identification of environmental factors in pediatric pedestrian motor vehicle crashes. J Trauma Inj Infect Critic Care 60(6):1384–1385

    Article  Google Scholar 

  • Su S, Tian Z, Liang S et al (2020) A reputation management scheme for efficient malicious vehicle identification over 5G networks. IEEE Wirel Commun 27(3):46–52

    Article  Google Scholar 

  • Tan Q, Romero RA (2018) Ground vehicle target signature identification with cognitive automotive radar (CARr) using 24-25 GHz and 76-77 GHz bands. IET radar sonar? Navigation 12(12):1448–1465

    Article  Google Scholar 

  • Valenzuela IC, Tolentino L, Juan R (2019) Utilization of e-nose sensory modality as add-on feature for advanced driver assistance system. Int J Adva Trends Comput Sci Eng 8(4):1783–1788

    Google Scholar 

  • Xiong S, Hui X, Tong Q (2018) The impact of an Eco-driving driver assistance system in driving simulator on the driving style. IFAC-Papers OnLine 51(31):331–336

    Article  Google Scholar 

  • Yijian ZH et al (2020) Safety and efficacy of percutaneous kyphoplasty assisted with O-arm navigation for the treatment of osteoporotic vertebral compression fractures at T6 to T9 vertebrae. Int Orthop 44(2):349–355

    Article  Google Scholar 

  • Zakaria MF et al (2017) Bus driving assistance system for town area by using ATmega328P microcontroller. AIP Conference Proceedings, 1883(1):1–8.

  • Zhao J, Wang X (2019) Vehicle-logo recognition based on modified HU invariant moments and SVM. Multimed Tools Appl 78(1):75–97

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, Shaoxing University, Shaoxing, 312000, Zhejiang, China

    Liang Chen

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  1. Liang Chen
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Correspondence to Liang Chen.

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Communicated by Suresh Chandra Satapathy.

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Chen, L. Road vehicle recognition algorithm in safety assistant driving based on artificial intelligence. Soft Comput 27, 1153–1162 (2023). https://doi.org/10.1007/s00500-021-06011-w

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  • DOI: https://doi.org/10.1007/s00500-021-06011-w

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