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Deep CNN-based autonomous system for safety measures in logistics transportation

A Correction to this article was published on 02 July 2021

This article has been updated


The careless activity of drivers in logistics transportation is a primary reason inside the vehicle during road accidents. This research aims to reduce the number of accidents caused by a failure of the driver in logistics transportation by incorporating an autonomous system. We propose a convolutional neural network -based architecture to recognize and classify different positions which cause road accidents. The proposed system is evaluated with the State Farm Distracted Driver Database, which included examples illustrating ten different driving positions like reaching behind and talking to the passenger, making up, safe driving, talking on the phone, clothing, checking right/left hand, right/left hand, and running the radio. The proposed approach has also been tested against recent algorithms and evaluated. Our model has obtained 98.98% accuracy compared to other types of approaches with different descriptors and classification techniques

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Change history


  • Albahli S, Rauf HT, Arif M, Nafis MT, Algosaibi A (2019) Identification of thoracic diseases by exploiting deep neural networks. Neural Networks 5:6

    Google Scholar 

  • Albahli S, Rauf HT, Algosaibi A, Balas VE (2021) Ai-driven deep cnn approach for multi-label pathology classification using chest x-rays. PeerJ Computer Science 7:e495

    Article  Google Scholar 

  • Artan Y, Balcı B, Elihoş A, Alkan B (2019) Vision based driver smoking behavior detection using surveillance camera images. In: Lecture notes in computer science, Springer International Publishing, pp 468–476.

  • Baheti B, Gajre S, Talbar S (2018) Detection of distracted driver using convolutional neural network. In: 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), IEEE.

  • Bichicchi A, Belaroussi R, Simone A, Vignali V, Lantieri C, Li X (2020) Analysis of road-user interaction by extraction of driver behavior features using deep learning. IEEE Access 8:19638–19645. doi: 10.1109/access.2020.2965940

    Article  Google Scholar 

  • Boureau YL, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE.

  • Chawan PM, Satardekar S, Shah D, Badugu R, Pawar A (2018) Distracted driver detection and classification. International Journal of Engineering Research and Applications 4:7.

    Article  Google Scholar 

  • Dhakate KR, Dash R (2020) Distracted driver detection using stacking ensemble. In: 2020 IEEE international students conference on electrical, electronics and computer science (SCEECS), IEEE.

  • Duan J, Li SE, Guan Y, Sun Q, Cheng B (2020) Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data. IET Intelligent Transport Systems 14(5):297–305.

    Article  Google Scholar 

  • Gao J, Wang H, Shen H (2020a) Machine learning based workload prediction in cloud computing. In: 2020 29th international conference on computer communications and networks (ICCCN), IEEE, pp 1–9

  • Gao J, Wang H, Shen H (2020b) Smartly handling renewable energy instability in supporting a cloud datacenter. In: 2020 IEEE international parallel and distributed processing symposium (IPDPS), IEEE, pp 769–778

  • Gao J, Wang H, Shen H (2020c) Task failure prediction in cloud data centers using deep learning. IEEE Trans Serv Comput

  • Gheisari M, Najafabadi HE, Alzubi JA, Gao J, Wang G, Abbasi AA, Castiglione A (2021) Obpp: an ontology-based framework for privacy-preserving in iot-based smart city. Future Gen Comput Syst

  • Hu H, Liu B, Zhang P (2017) Several models and applications for deep learning. In: 2017 3rd IEEE international conference on computer and communications (ICCC), IEEE.

  • Hu Y, Lu M, Lu X (2018) Driving behaviour recognition from still images by using multi-stream fusion CNN. Machine Vision and Applications 30(5):851–865.

    Article  Google Scholar 

  • Huang C, Wang X, Cao J, Wang S, Zhang Y (2020) HCF: A hybrid CNN framework for behavior detection of distracted drivers. IEEE Access 8:109335–109349.

    Article  Google Scholar 

  • Ito M, Fukumi M, Sato K (2013) Analysis of safety verification behavior and classification of driver’s head posture. In: 2013 IEEE international conference on mechatronics and automation, IEEE, pp 884–889.

  • Jin B, Cruz L, Goncalves N (2020) Deep facial diagnosis: Deep transfer learning from face recognition to facial diagnosis. IEEE Access 8:123649–123661

    Article  Google Scholar 

  • Kato T, Fujii T, Tanimoto M (2004) Detection of driver’s posture in the car by using far infrared camera. In: IEEE intelligent vehicles symposium, 2004, IEEE, pp 339–344.

  • Khan MN, Ahmed MM (2020) Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data. Accident Analysis & Prevention 142:105521.

    Article  Google Scholar 

  • Khan S, Yong SP (2017) A deep learning architecture for classifying medical images of anatomy object. In: Pacific signal and information processing association annual summit and conference (APSIPA ASC), IEEE.

  • Kuutti S, Bowden R, Jin Y, Barber P, Fallah S (2020) A survey of deep learning applications to autonomous vehicle control. IEEE Trans Intell Transp Syst, doi: 10.1109/tits.2019.2962338

    Article  Google Scholar 

  • Liu X, Zhu Y, Fujimura K (2002) Real-time pose classification for driver monitoring. In: Proceedings. The IEEE 5th international conference on intelligent transportation systems, IEEE.

  • Lu M, Hu Y, Lu X (2019a) Dilated light-head r-CNN using tri-center loss for driving behavior recognition. Image and Vision Computing 90:103800.

    Article  Google Scholar 

  • Lu M, Hu Y, Lu X (2019b) Dilated light-head r-CNN using tri-center loss for driving behavior recognition. Image and Vision Computing 90:103800.

    Article  Google Scholar 

  • Lu M, Hu Y, Lu X (2019c) Driver action recognition using deformable and dilated faster r-CNN with optimized region proposals. Applied Intelligence 50(4):1100–1111.

    Article  Google Scholar 

  • Malik S, Khattak HA, Ameer Z, Shoaib U, Rauf HT, Song H (2021) Proactive scheduling and resource management for connected autonomous vehicles: a data science perspective. IEEE Sens J

  • Meraj T, Rauf HT, Zahoor S, Hassan A, Lali MI, Ali L, Bukhari SAC, Shoaib U (2019) Lung nodules detection using semantic segmentation and classification with optimal features. Neural Comput Appl:1–14

  • Moslemi N, Azmi R, Soryani M (2019) Driver distraction recognition using 3d convolutional neural networks. In: 2019 4th international conference on pattern recognition and image analysis (IPRIA), IEEE.

  • Oliver N, Pentland A (2000) Graphical models for driver behavior recognition in a SmartCar. In: Proceedings of the IEEE intelligent vehicles symposium 2000 (Cat. No.00TH8511), IEEE, pp 7–12.

  • Preprint repository arXiv achieves milestone million uploads (2014) Physics Today

  • Rauf HT, Lali MIU, Zahoor S, Shah SZH, Rehman AU, Bukhari SAC (2019) Visual features based automated identification of fish species using deep convolutional neural networks. Computers and Electronics in Agriculture 167:105075.

    Article  Google Scholar 

  • Rauf HT, Malik S, Shoaib U, Irfan MN, Lali MI (2020) Adaptive inertia weight bat algorithm with sugeno-function fuzzy search. Applied Soft Computing 90:106159

    Article  Google Scholar 

  • Shahverdy M, Fathy M, Berangi R, Sabokrou M (2020) Driver behavior detection and classification using deep convolutional neural networks. Expert Systems with Applications 149:113240.

    Article  Google Scholar 

  • Shin D, Geun Kim H, MoonPark K, Yi K (2019) Development of deep learning based human-centered threat assessment for application to automated driving vehicle. Applied Sciences 10(1):253.

    Article  Google Scholar 

  • Toma MI, Rothkrantz LJ, Antonya C (2012) Car driver skills assessment based on driving postures recognition. In: 2012 IEEE 3rd international conference on cognitive infocommunications (CogInfoCom), IEEE.

  • Tran C, Doshi A, Trivedi MM (2012) Modeling and prediction of driver behavior by foot gesture analysis. Computer Vision and Image Understanding 116(3):435–445.

    Article  Google Scholar 

  • Tran D, Do HM, Sheng W, Bai H, Chowdhary G (2018) Real-time detection of distracted driving based on deep learning. IET Intelligent Transport Systems 12(10):1210–1219.

    Article  Google Scholar 

  • Valiente R, Zaman M, Ozer S, Fallah YP (2019) Controlling steering angle for cooperative self-driving vehicles utilizing CNN and LSTM-based deep networks. In: IEEE intelligent vehicles symposium (IV), IEEE.

  • Valiente R, Zaman M, Fallah YP, Ozer S (2020) Connected and autonomous vehicles in the deep learning era: a case study on computer-guided steering. In: Handbook of pattern recognition and computer vision, World Scientific, pp 365–384.

  • Veeraraghavan H, Bird N, Atev S, Papanikolopoulos N (2007) Classifiers for driver activity monitoring. Transportation Research Part C: Emerging Technologies 15(1):51–67.

    Article  Google Scholar 

  • Xing Y, Lv C, Wang H, Cao D, Velenis E (2020) An ensemble deep learning approach for driver lane change intention inference. Transportation Research Part C: Emerging Technologies 115:102615.

    Article  Google Scholar 

  • Yadav S, Patwa A, Rane S, Narvekar C (2019) Indian traffic signboard recognition and driver alert system using machine learning. International Journal of Applied Sciences and Smart Technologies 1(1):1–10.

    Article  Google Scholar 

  • Yan C, Zhang B, Coenen F (2015) Driving posture recognition by convolutional neural networks. In: 2015 11th international conference on natural computation (ICNC), IEEE.

  • You Z, Gao Y, Zhang J, Zhang H, Zhou M, Wu C (2017) A study on driver fatigue recognition based on SVM method. In: 2017 4th international conference on transportation information and safety (ICTIS), IEEE.

  • Younis MC (2021) Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction. Comput Med Imaging Graphics:101921

  • Younis MC, Abuhammad H (2021) A hybrid fusion framework to multi-modal bio metric identification. Multimed Tools Appl:1–24

  • Zhao C, He J, Zhu T, Lian J, Shen J, Zhang H (2011a) Recognition of driver’s fatigue expressions by gabor wavelet transform and multilayer perceptron classifier. In: Proceedings 2011 international conference on transportation, mechanical, and electrical engineering (TMEE), IEEE, pp 617–620.

  • Zhao C, Zhang B, Lian J, He J, Lin T, Zhang X (2011b) Classification of driving postures by support vector machines. In: 2011 sixth international conference on image and graphics, IEEE.

  • Zhao C, Zhang B, He J, Lian J (2012) Recognition of driving postures by contourlet transform and random forests. IET Intelligent Transport Systems 6(2):161.

    Article  Google Scholar 

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Authors and Affiliations



All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [AR, AM, YC, HTR, and SK]. The first draft of the manuscript was written by [AR], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hafiz Tayyab Rauf.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by Vicente Garcia Diaz.

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The original article has been updated: Due to corresponding author name update.

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Rouari, A., Moussaoui, A., Chahir, Y. et al. Deep CNN-based autonomous system for safety measures in logistics transportation. Soft Comput 25, 12357–12370 (2021).

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  • Autonomous system
  • Logistics transportation
  • Convolutional neural network
  • Deep learning
  • Safety measures