{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:15:27Z","timestamp":1776215727163,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,25]],"date-time":"2021-12-25T00:00:00Z","timestamp":1640390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid expansion of a country\u2019s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system\u2019s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system\u2019s infrastructure is far too low-cost and easy to install.<\/jats:p>","DOI":"10.3390\/s22010126","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7212-717X","authenticated-orcid":false,"given":"Md. Kalim Amzad","family":"Chy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong 4210, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Kadar Muhammad","family":"Masum","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong 4210, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6317-0360","authenticated-orcid":false,"given":"Kazi Abdullah Mohammad","family":"Sayeed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong 4210, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-1834","authenticated-orcid":false,"given":"Md Zia","family":"Uddin","sequence":"additional","affiliation":[{"name":"Software and Service Innovation Department, SINTEF Digital, 0316 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,25]]},"reference":[{"key":"ref_1","unstructured":"Maasum, A.K.M., Chy, M.K.A., Rahman, I., Uddin, M.N., and Azam, K.I. (2018, January 27\u201328). An Internet of Things (IoT) based smart traffic management system: A context of Bangladesh. Proceedings of the 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Taj, F.W., Masum, A.K.M., Reza, S.T., Chy, M.K.A., and Mahbub, I. (2018, January 27\u201328). Automatic accident detection and human rescue system: Assistance through communication technologies. Proceedings of the 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh.","DOI":"10.1109\/ICISET.2018.8745570"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3761","DOI":"10.1007\/s11053-021-09895-5","article-title":"A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters","volume":"30","author":"Najafzadeh","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4619","DOI":"10.1007\/s10462-021-10007-1","article-title":"Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: Integration of remote sensing and data-driven models","volume":"54","author":"Najafzadeh","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1007\/s11269-021-02911-6","article-title":"Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology Based on Global Positioning System and Data Driven Techniques","volume":"35","author":"Najafzadeh","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Saberi-Movahed, F., Mohammadifard, M., Mehrpooya, A., Rezaei-Ravari, M., Berahmand, K., Rostami, M., Karami, S., Najafzadeh, M., Hajinezhad, D., and Jamshidi, M. (2021). Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods. medRxiv.","DOI":"10.1101\/2021.07.07.21259699"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.future.2021.06.023","article-title":"A study on the AI-based online triage model for hospitals in sustainable smart city","volume":"125","author":"Kong","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","first-page":"1","article-title":"AI-empowered IoT security for smart cities","volume":"21","author":"Lv","year":"2021","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_9","unstructured":"Masum, A.K.M., Chy, M.K.A., Hasan, M.T., Sayeed, M.H., and Reza, S.T. (2019, January 14\u201316). Smart Meter with Load Prediction Feature for Residential Customers in Bangladesh. Proceedings of the 2019 International Conference on Energy and Power Engineering (ICEPE), Dhaka, Bangladesh."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Masum, A.K.M., Saveed, M.H., Chy, M.K.A., Hasan, M.T., and Reza, S.T. (2019, January 7\u20139). Design and Implementation of Smart Meter with Load Forecasting Feature for Residential Customers. Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019sBazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679357"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110209","DOI":"10.1109\/ACCESS.2021.3102227","article-title":"Big Data and AI Revolution in Precision Agriculture: Survey and Challenges","volume":"9","author":"Bhat","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.copbio.2020.09.003","article-title":"The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems","volume":"70","author":"Jung","year":"2021","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_13","unstructured":"Chy, M.K.A., Masum, A.K.M., Hossain, M.E., Alam, M.G.R., Khan, S.I., and Alam, M.S. (2020). A Low-Cost Ideal Fish Farm Using IoT: In the Context of Bangladesh Aquaculture System. Inventive Communication and Computational Technologies, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100025","DOI":"10.1016\/j.caeai.2021.100025","article-title":"AI technologies for education: Recent research & future directions","volume":"2","author":"Zhang","year":"2021","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Elshafey, A.E., Anany, M.R., Mohamed, A.S., Sakr, N., and Aly, S.G. (2021). Dr. Proctor: A Multi-modal AI-Based Platform for Remote Proctoring in Education. Artificial Intelligence in Education, Proceedings of the International Conference on Artificial Intelligence in Education, Utrecht, The Netherlands, 14\u201318 June 2021, Springer.","DOI":"10.1007\/978-3-030-78270-2_26"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, D., and Yoon, S.N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010271"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Davahli, M.R., Karwowski, W., Fiok, K., Wan, T., and Parsaei, H.R. (2021). Controlling Safety of Artificial Intelligence-Based Systems in Healthcare. Symmetry, 13.","DOI":"10.20944\/preprints202012.0313.v2"},{"key":"ref_18","unstructured":"Apell, P., and Eriksson, H. (2021). Artificial intelligence (AI) healthcare technology innovations: The current state and challenges from a life science industry perspective. Technol. Anal. Strateg. Manag., 1\u201315."},{"key":"ref_19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fang, H., Gupta, S., Iandola, F., Srivastava, R.K., Deng, L., Doll\u00e1r, P., Gao, J., He, X., Mitchell, M., and Platt, J.C. (2015, January 7\u201312). From captions to visual concepts and back. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298754"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_23","unstructured":"U.S. Department of Transportation (2019, April 03). Automated Driving Systems\u2014A Vision for Safety., Available online: https:\/\/www.nhtsa.gov\/sites\/nhtsa.dot.gov\/files\/documents\/13069a-ads2.0_090617_v9a_tag.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113816","DOI":"10.1016\/j.eswa.2020.113816","article-title":"Self-driving cars: A survey","volume":"165","author":"Badue","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MC.2017.4451204","article-title":"Self-driving cars","volume":"50","author":"Daily","year":"2017","journal-title":"Computer"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alam, S., Sulistyo, S., Mustika, I.W., and Adrian, R. (2019, January 16\u201317). Review of potential methods for handover decision in v2v vanet. Proceedings of the 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Jember, Indonesia.","DOI":"10.1109\/ICOMITEE.2019.8921117"},{"key":"ref_27","unstructured":"Baza, M., Nabil, M., Mahmoud, M.M.E.A., Bewermeier, N., Fidan, K., Alasmary, W., and Abdallah, M. (2020). Detecting sybil attacks using proofs of work and location in vanets. IEEE Trans. Dependable Secur. Comput."},{"key":"ref_28","unstructured":"Schmittner, C., Chlup, S., Fellner, A., Macher, G., and Brenner, E. (2020, January 10\u201311). ThreatGet: Threat modeling based approach for automated and connected vehicle systems. Proceedings of the AmE 2020-Automotive meets Electronics; 11th GMM-Symposium, Dortmund, Germany."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101823","DOI":"10.1016\/j.adhoc.2018.12.006","article-title":"A review on safety failures, security attacks, and available countermeasures for autonomous vehicles","volume":"90","author":"Cui","year":"2019","journal-title":"Ad Hoc Networks"},{"key":"ref_30","unstructured":"Dibaei, M., Zheng, X., Jiang, K., Maric, S., Abbas, R., Liu, S., Zhang, Y., Deng, Y., Wen, S., and Zhang, J. (2019). An overview of attacks and defences on intelligent connected vehicles. arXiv, preprint."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Levine, W.S. (2018). The Control Handbook, CRC Press.","DOI":"10.1201\/b10382"},{"key":"ref_32","unstructured":"Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016). End to end learning for self-driving cars. arXiv, preprint."},{"key":"ref_33","first-page":"1334","article-title":"End-to-end training of deep visuomotor policies","volume":"17","author":"Levine","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","unstructured":"Pomerleau, D.A. (1989). Alvinn: An Autonomous Land Vehicle in a Neural Network, Carnegie-Mellon University. Artificial Intelligence And Psychology Project."},{"key":"ref_35","unstructured":"Lecun, Y., Cosatto, E., Ben, J., Muller, U., and Flepp, B. (2019, February 15). Dave: Autonomous Off-Road Vehicle Control Using End-to-End Learning, Available online: https:\/\/cs.nyu.edu\/~yann\/research\/dave\/."},{"key":"ref_36","unstructured":"N.T. Report (2019, May 23). GPU-Based Deep Learning Inference: A Performance and Power Analysis., Available online: http:\/\/developer.download.nvidia.com\/embedded\/jetson\/TX1\/docs\/jetson_tx1_whitepaper.pdf?autho=1447264273_0fafa14fcc7a1f685769494ec9b0fcad&file=jetson_tx1_whitepaper.pdf."},{"key":"ref_37","unstructured":"Masum, A.K.M., Rahman, M.A., Abdullah, M.S., Chowdhury, S.B.S., Khan, T.B.F., and Raihan, M.K. (2019, January 15\u201317). A Supervised Learning Approach to An Unmanned Autonomous Vehicle. Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India."},{"key":"ref_38","unstructured":"Stavens, D., and Thrun, S. (2012). A self-supervised terrain roughness estimator for off-road autonomous driving. arXiv, preprint."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1002\/rob.20276","article-title":"Learning long-range vision for autonomous off-road driving","volume":"26","author":"Hadsell","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.trc.2017.08.029","article-title":"Development and validation of a questionnaire to assess pedestrian receptivity toward fully autonomous vehicles","volume":"84","author":"Deb","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5382192","DOI":"10.1155\/2018\/5382192","article-title":"Acceptance of driverless vehicles: Results from a large cross-national questionnaire study","volume":"2018","author":"Nordhoff","year":"2018","journal-title":"J. Adv. Transp."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.tra.2017.08.005","article-title":"Automated vehicles and behavioural adaptation in Canada","volume":"104","author":"Robertson","year":"2017","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Reke, M., Peter, D., Schulte-Tigges, J., Schiffer, S., Ferrein, A., Walter, T., and Matheis, D. (2020, January 29\u201331). A self-driving car architecture in ROS2. Proceedings of the 2020 International SAUPEC\/RobMech\/PRASA Conference, Cape Town, South Africa.","DOI":"10.1109\/SAUPEC\/RobMech\/PRASA48453.2020.9041020"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106948","DOI":"10.1016\/j.asoc.2020.106948","article-title":"AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles","volume":"99","author":"Bakioglu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"102214","DOI":"10.1016\/j.tre.2020.102214","article-title":"The adoption of self-driving delivery robots in last mile logistics","volume":"146","author":"Chen","year":"2021","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10462-018-9631-5","article-title":"Artificial intelligence test: A case study of intelligent vehicles","volume":"50","author":"Li","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.1016\/j.aej.2021.08.029","article-title":"Multimodal transport distribution model for autonomous driving vehicles based on improved ALNS","volume":"61","author":"Guo","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106256","DOI":"10.1016\/j.aap.2021.106256","article-title":"Young and older adult pedestrians\u2019 behavior when crossing a street in front of conventional and self-driving cars","volume":"159","author":"Merlhiot","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104257","DOI":"10.1016\/j.engappai.2021.104257","article-title":"Giving commands to a self-driving car: How to deal with uncertain situations?","volume":"103","author":"Deruyttere","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., and Lowe, D.G. (2017, January 21\u201326). Unsupervised learning of depth and ego-motion from video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.700"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.iatssr.2016.11.002","article-title":"Human-like motion planning model for driving in signalized intersections","volume":"41","author":"Gu","year":"2017","journal-title":"IATSS Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.trc.2015.09.011","article-title":"Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions","volume":"60","author":"Katrakazas","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mostafa, M.S.B., Masum, A.K.M., Uddin, M.S., Chy, M.K.A., and Reza, S.T. (2019, January 7\u20139). Amphibious Line following Robot for Product Delivery in Context of Bangladesh. Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019sBazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679260"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Colak, I., and Yildirim, D. (2009, January 3\u20135). Evolving a Line Following Robot to use in shopping centers for entertainment. Proceedings of the 2009 35th Annual Conference of IEEE Industrial Electronics, Porto, Portugal.","DOI":"10.1109\/IECON.2009.5415369"},{"key":"ref_55","first-page":"27","article-title":"Design and fabrication of line follower robot","volume":"2","author":"Islam","year":"2013","journal-title":"Asian J. Appl. Sci. Eng."},{"key":"ref_56","first-page":"2446","article-title":"Development and applications of line following robot based health care management system","volume":"2","author":"Punetha","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET)"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1177\/0278364917696568","article-title":"Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving","volume":"36","author":"Wolcott","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ahmad, T., Ilstrup, D., Emami, E., and Bebis, G. (2017, January 11\u201314). Symbolic road marking recognition using convolutional neural networks. Proceedings of the 2017 IEEE intelligent vehicles symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995910"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Greenhalgh, J., and Mirmehdi, M. (2015, January 10\u201312). Detection and Recognition of Painted Road Surface Markings. Proceedings of the ICPRAM (1), Lisbon, Portugal.","DOI":"10.5220\/0005273501300138"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hyeon, D., Lee, S., Jung, S., Kim, S.-W., and Seo, S.-W. (2016, January 19\u201322). Robust road marking detection using convex grouping method in around-view monitoring system. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535511"},{"key":"ref_61","unstructured":"Chollet, F. (2015). Keras, Github. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1504\/IJATM.2019.098513","article-title":"Examining the myths of connected and autonomous vehicles: Analysing the pathway to a driverless mobility paradigm","volume":"19","author":"Nikitas","year":"2019","journal-title":"Int. J. Automot. Technol. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3389\/frsc.2019.00002","article-title":"Governing cities for sustainability: A research agenda and invitation","volume":"1","author":"Evans","year":"2019","journal-title":"Front. Sustain. Cities"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.wasman.2020.03.015","article-title":"Current status and perspectives on recycling of end-of-life battery of electric vehicle in Korea (Republic of)","volume":"106","author":"Choi","year":"2020","journal-title":"Waste Manag."},{"key":"ref_65","first-page":"214","article-title":"The Security and Privacy In Your Car Act: Will It Actually Protect You?","volume":"18","author":"Bollinger","year":"2017","journal-title":"North Carol. J. Law Technol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lim, H.S.M., and Taeihagh, A. (2018). Autonomous vehicles for smart and sustainable cities: An in-depth exploration of privacy and cybersecurity implications. Energies, 11.","DOI":"10.3390\/en11051062"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"101408","DOI":"10.1016\/j.giq.2019.101408","article-title":"Does high e-government adoption assure stronger security? Results from a cross-country analysis of Australia and Thailand","volume":"37","author":"Thompson","year":"2020","journal-title":"Gov. Inf. Q."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"105664","DOI":"10.1016\/j.aap.2020.105664","article-title":"Safety assessment of highly automated driving systems in test tracks: A new framework","volume":"144","author":"Feng","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.trc.2018.02.012","article-title":"Autonomous vehicle perception: The technology of today and tomorrow","volume":"89","author":"Gruyer","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_70","unstructured":"SullyChen (2019, April 03). Autopilot-TensorFlow. Available online: https:\/\/github.com\/SullyChen\/Autopilot-TensorFlow."},{"key":"ref_71","unstructured":"Apollo (2019, April 03). Apollo Data Open Platform. Available online: http:\/\/data.apollo.auto\/?locale=en-us&lang=en."},{"key":"ref_72","unstructured":"Santana, E., and Hotz, G. (2016). Learning a driving simulator. arXiv, preprint."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yin, H., and Berger, C. (2017, January 16\u201319). When to use what data set for your self-driving car algorithm: An overview of publicly available driving datasets. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317828"},{"key":"ref_74","unstructured":"Udacity (2019, May 05). Self-Driving-Car. Available online: https:\/\/github.com\/udacity\/self-driving-car\/tree\/master\/datasets."},{"key":"ref_75","unstructured":"Udacity (2019, May 05). Self-Driving-Car-Sim. Available online: https:\/\/github.com\/udacity\/self-driving-car-sim."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:53:18Z","timestamp":1760169198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,25]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010126"],"URL":"https:\/\/doi.org\/10.3390\/s22010126","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,25]]}}}