Palabras clave
inteligencia Artificial
Seguridad vial
Toma de decisiones éticas
Conectividad
Infraestructura
Movilidad
Cómo citar
Resumen
El artículo examina el desarrollo actual de la tecnología de vehículos autónomos, subrayando su potencial para reducir los accidentes de tránsito, mejorar la eficiencia y acceso a la movilidad del tráfico. Sin embargo, la implementación de esta tecnología disruptiva plantea grandes desafíos en diversos órdenes. La incertidumbre jurídica en el ámbito de responsabilidad civil en caso de accidentes, la privacidad de los datos y dilemas éticos concernientes con la toma de decisiones, son algunos problemas que acompañan esta tecnología. Además, requerimientos en cuanto modernización de la infraestructura urbana para dar soporte a estos sistemas. Mediante una metodología cualitativa basada en la revisión bibliográfica y análisis de casos, este estudio ofrece una perspectiva integral sobre aquellos elementos que influyen en la implementación de vehículos autónomos. Los hallazgos enfatizan la importancia de la colaboración entre la industria, el gobierno y la sociedad para garantizar una transición hacia una movilidad segura, inclusiva y sostenible
Citas
Ali, A., Jianjun, H., & Jabbar, A. (2025). Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges. IEEE Access, 13, 80637-80665. https://doi.org/10.1109/ACCESS.2025.3562128
Banish, G. (2024). Innovation and Technology: The Era of Autonomous Cars and Their Outcomes in Law Enforcement. https://digitalcommons.liberty.edu/doctoral/5759
Beanland, V., Ritchie, C., Ousset, C., Galland, B. C., & Schaughency, E. A. (2024). Distracted and unfocused driving in supervised and unsupervised teen drivers: associations with sleep, inattention, and cognitive disengagement syndrome symptoms. Transportation research part F: traffic psychology and behaviour, 100, 169-180. https://doi.org/10.1016/j.trf.2023.11.013
Bilici, F., & Türkoğlu, İ. K. (2024). Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 36(1), 383-407. https://doi.org/10.35234/fumbd.1385541
Brühl, T., Ewecker, L., Schwager, R., Sohn, T. S., & Hohmann, S. (2024). Making Radar Detections Safe for Autonomous Driving: A Review. VEHITS, 299-310. https://doi.org/10.5220/0012630400003702
Chaudhary, S., Sharma, A., Khichar, S., Meng, Y., & Malhotra, J. (2024). Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios. Scientific Reports, 14(1), 17339. https://doi.org/10.1038/s41598-024-66850-z
Chengula, T. J., Mwakalonge, J., Comert, G., Sulle, M., Siuhi, S., & Osei, E. (2024). Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection. Machine Learning with Applications, 17, 100580. https://doi.org/10.1016/j.mlwa.2024.100580
Clayton, J. (2024). How robotaxis are dividing San Francisco. BBC news. https://www.bbc.com/news/technology-66611513
Dai, Z., Guan, Z., Chen, Q., Xu, Y., & Sun, F. (2024). Enhanced Object Detection in Autonomous Vehicles through LiDAR—Camera Sensor Fusion. World Electric Vehicle Journal, 15(7). https://doi.org/10.3390/wevj15070297
Donà, R., Mattas, K., Albano, G., Váss, S., & Ciuffo, B. (2024, September). Towards Automated Driving: Findings and Comparison with ADAS. In Advanced Vehicle Control Symposium (pp. 954-960). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70392-8_134
Dumitrascu, D. I. (2024). Influence of Road Infrastructure Design over the Traffic Accidents: A Simulated Case Study. Infrastructures, 9(9), 154. https://doi.org/10.3390/infrastructures9090154
Ekatpure, R. (2024). Safety Protocols and Risk Mitigation Strategies in the Implementation of Autonomous Driving Systems. Advances in Urban Resilience and Sustainable City Design, 16(02), 37-46. https://orientreview.com/index.php/aurscd-journal/article/view/76
Ferdman, A. (2024). Bowling alone in the autonomous vehicle: The ethics of well-being in the driverless car. AI & SOCIETY, 39(3), 1171-1183. https://doi.org/10.1007/s00146-022-01565-1
Fu, S., Yang, Z., Ma, Y., Li, Z., Xu, L., & Zhou, H. (2024). Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review. Applied Sciences, 14(7), 3016. https://doi.org/10.3390/app14073016
Fu, H., Ye, S., Fu, X., Chen, T., & Zhao, J. (2025). New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records. Travel Behaviour and Society, 38, 100934. https://doi.org/10.1016/j.tbs.2024.100934
Gao, J. (2024). Autonomous Car Behavioral Training Using Deep Neural Network. Journal of Computing and Electronic Information Management, 12(1), 48-53. https://doi.org/10.54097/mkny71cuq7
Hell, M., Hajgató, G., Bogár-Németh, Á., & Bári, G. (2024, June). A lidar-based approach to autonomous racing with model-free reinforcement learning. In 2024 IEEE Intelligent Vehicles Symposium (IV) (pp. 258-263). IEEE. https://doi.org/10.1109/IV55156.2024.10588613
Huang, C., Wen, X., & He, D. (2024). Characteristics of rear-end collisions: a comparison between automated driving system-involved crashes and advanced driving assistance system-involved crashes. Transportation research record, 2678(7), 771-782. https://doi.org/10.1177/03611981231209319
Islayem, R., Alhosani, F., Hashem, R., Alzaabi, A., & Meribout, M. (2024). Hardware Accelerators for Autonomous Cars: A Review. arXiv preprint arXiv:2405.00062. https://doi.org/10.48550/arXiv.2405.00062
Jamali, L. (2024). Tesla shares slide after Cybercab robotaxi revealed. BBC news. https://www.bbc.com/news/articles/cm29x5ke9jdo
Jaulkar, S., & Parihar, A. (2025). Different types of injury associated with road traffic accidents. Multidisciplinary Reviews, 8(11). https://doi.org/10.31893/multirev.2025111
Jedličková, A. (2024). Ethical approaches in designing autonomous and intelligent systems: a comprehensive survey towards responsible development. AI & SOCIETY, 1-14. https://doi.org/10.1007/s00146-024-02040-9
Jiang, E., Krishnamurthy, H. M., Nguyen, H., Hao, H., Miao, Y., & Zhang, P. (2024). The PennSTART Safety Standards Project: Current Safety Standards and Test Track Designs for Connected and Autonomous Vehicles (No. 467). Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/77712/dot_77712_DS1.pdf
Jones, W. D. (2024). Partial Automation Doesn't Make Vehicles Safer Self-driving tech is better treated as a convenience, not a safety feature. Recuperado de: https://spectrum.ieee.org/partial-vehicle-autonomy-risk
Katoch, B., Ghosh, I., & Chandra, S. (2025). Safety of children in school zones− A systematic review. Transportation Research Part F: Traffic Psychology and Behaviour, 113, 554-569. https://doi.org/10.1016/j.trf.2025.05.020
Krügel, S., & Uhl, M. (2024). The risk ethics of autonomous vehicles: an empirical approach. Scientific reports, 14(1), 960. https://doi.org/10.1038/s41598-024-51313-2
Lee, Y., & Park, M. (2025). Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles. Applied Sciences, 15(1), 2076-3417. https://doi.org/10.3390/app15010419
Li, Y., Cao, P., Xia, W., Zhou, J., Chu, Y., Zhang, W., & Zhang, J. (2024). Radar High-Speed Target Range-Doppler-Azimuth Coherent Extension Detection for Autonomous Vehicles. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2024.3409886
Lisowski, J. (2024). Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving. Electronics, 13(6), 1065. https://doi.org/10.3390/electronics13061065
Márquez, D. J. E., Prieto, M. A., Castañeda, R. L. Y Benavides, R. L. (2024). Industria 4.0: Internet de las cosas, Ciberseguridad y Aplicaciones. Márquez, D. J (Compilador). Editorial Universidad de Cundinamarca.
https://repositorioctei.ucundinamarca.edu.co/ingenieria/3
Márquez-Díaz, J. E. (2022). Cybersecurity and Internet of Things. Outlook for this Decade. Computación y Sistemas, 26(3), 1191–1204. https://doi.org/10.13053/CyS-26-3-3925
Mesías, C. D. (2025). Implementación de redes vehiculares (VANETs) para soporte de comunicaciones en vehículos autónomos. Polo del Conocimiento, 10(1), 2497-2511. https://doi.org/10.23857/pc.v10i1.8825
Mofolasayo, A. (2024). Towards ‘vision-zero’in road traffic fatalities: the need for reasonable degrees of automation to complement human efforts in driving operation. Systems, 12(2), 40. https://doi.org/10.3390/systems12020040
Moraes, P., Peters, C., Da Rosa, A., Melgar, V., Nuñez, F., Retamar, M., William Moraes, W., Saravia, V., Sodre, H., Barcelona, S., Scirgalea, A., Deniz, J., Guterres, J., Kelbouscas, & Grando, R. (2024). UruBots Autonomous Cars Team One Description Paper for FIRA 2024. arXiv preprint arXiv:2406.08745. https://doi.org/10.48550/arXiv.2406.08745
Mounabhargav, P., & Agrawal, P. (2024, March). Camera and LiDAR Integration for Lane-Following and Obstacle Avoidance in Self-Driving Cars. In 2024 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-6). IEEE. https://doi.org/10.1109/ESCI59607.2024.10497314
Murtaza, M., Cheng, C. T., Fard, M., & Zeleznikow, J. (2024). Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance. Applied Sciences, 14(6), 2348. https://doi.org/10.3390/app14062348
Nour, M., Nour, M., & Zaki, M. H. (2025). Integrating Vehicle-to-Infrastructure Communication for Safer Lane Changes in Smart Work Zones. World Electric Vehicle Journal, 16(4), 215. https://doi.org/10.3390/wevj16040215
Nyholm, S. (2024). Ethical and Legal Issues Related to Autonomous Vehicles. Future Law, Ethics, and Smart Technologies, 190-204. https://doi.org/10.1163/9789004682900_019
Oliva, F., Landolfi, E., Salzillo, G., Massa, A., D’Onghia, S. M., & Troiano, A. (2025). Implementation and Testing of V2I Communication Strategies for Emergency Vehicle Priority and Pedestrian Safety in Urban Environments. Sensors, 25(2), 485. https://doi.org/10.3390/s25020485
Orehovački, T., Oreški, G., & Šajina, R. (2024, May). Driving Habits and the Need for Fatigue and Attention Monitoring Devices: Insights from Croatian Drivers. In 2024 47th MIPRO ICT and Electronics Convention (MIPRO) (pp. 1427-1432). IEEE. https://doi.org/10.1109/MIPRO60963.2024.10569821
Park, S., & Park, H. (2024). PIER: cyber-resilient risk assessment model for connected and autonomous vehicles. Wireless Networks, 30(5), 4591-4605. https://doi.org/10.1007/s11276-022-03084-9
Peralta, R., Becerra, I., Ruiz, U., & Murrieta-Cid, R. (2024). A methodology for generating driving styles for autonomous cars. Journal of Intelligent Transportation Systems, 28(1), 120-140. https://doi.org/10.1080/15472450.2022.2109417
Pettigrew, S., Booth, L., Farrar, V., Brown, J., Karl, C., Godic, B., Vidanaarachchi, R., & Thompson, J. (2024). Public support for proposed government policies to optimise the social benefits of autonomous vehicles. Transport Policy, 149, 264-270. https://doi.org/10.1016/j.tranpol.2024.02.016
Pieroni, R., Specchia, S., Corno, M., & Savaresi, S. M. (2024). Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving. arXiv preprint arXiv:2403.04112. https://doi.org/10.48550/arXiv.2403.04112
Rahman, M. H., Gulzar, M. M., Haque, T. S., Habib, S., Shakoor, A., & Murtaza, A. F. (2025). Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms. Engineering Science and Technology, an International Journal, 64, 101950. https://doi.org/10.1016/j.jestch.2025.101950
Rana, K., & Khatri, N. (2024). Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review. Computers and Electrical Engineering, 117, 109237. https://doi.org/10.1016/j.compeleceng.2024.109237
Rao, Y. C., Satyanarayana, M., Lavanya, P., Chandra, G. R., Rao, L. S., & Srujan, A. S. (2025). Design of near-infrared imaging system using Nd-YAG laser at 1064 nm and gated InGaAs camera. Journal of Optics, 1-11. https://doi.org/10.1007/s12596-025-02548-3
Saber, E. M., Kostidis, S. C., & Politis, I. (2024). Ethical Dilemmas in Autonomous Driving: Philosophical, Social, and Public Policy Implications. In Deception in Autonomous Transport Systems: Threats, Impacts and Mitigation Policies (pp. 7-20). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-55044-7_2
Schleuning, D., Dunphy, J., & Verghese, S. (2024, March). Lidar for autonomous vehicles: trends in lasers and detectors. In High-Power Diode Laser Technology XXII (Vol. 12867, p. 1286702). SPIE. https://doi.org/10.1117/12.3010003
Schoner, J., Sanders, R., & Goddard, T. (2024). Effects of advanced driver assistance systems on impact velocity and injury severity: an exploration of data from the crash investigation sampling system. Transportation research record, 2678(5), 451-462. https://doi.org/10.1177/03611981231189740
Sivadharshan, T., Kalaivani, K., Golden Stepha, N., Rajitha Jasmine, R., Jasmine Gilda, A., & Godfrey, S. (2024). An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments. Artificial Intelligence for Autonomous Vehicles, 151-171. https://doi.org/10.1002/9781119847656.ch7
Song, H., Cho, J., Ha, J., Park, J., & Jo, K. (2024). Panoptic-FusionNet: Camera-LiDAR fusion-based point cloud panoptic segmentation for autonomous driving. Expert Systems with Applications, 251, 123950. https://doi.org/10.1016/j.eswa.2024.123950
Takala, J., Hämäläinen, P., Sauni, R., Nygård, C. H., Gagliardi, D., & Neupane, S. (2024). Global-, regional-and country-level estimates of the work-related burden of diseases and accidents in 2019. Scandinavian journal of work, environment & health, 50(2), 73. https://doi.org/10.5271/sjweh.4132
Vakili, E., Amirkhani, A., & Mashadi, B. (2024). DQN-based ethical decision-making for self-driving cars in unavoidable crashes: An applied ethical knob. Expert Systems with Applications, 255, 124569. https://doi.org/10.1016/j.eswa.2024.124569
Viadero, M. F., Rentería, A. L., Pérez-Oria, J., & Viadero, R. F. (2024). Radar-based pedestrian and vehicle detection and identification for driving assistance. Vehicles, 6(3), 1185-1199. https://doi.org/10.3390/vehicles6030056
Watts, M., Poulton, C., Byrd, M. and Smolka, G. (2023). Lidar on a Chip puts self-driving cars in the fast lane. Recuperado de: https://spectrum.ieee.org/lidar-on-a-chip
Watkins, S. J., & Musselwhite, C. (2025). Recognised cognitive biases: How far do they explain transport behaviour?. Journal of Transport & Health, 40, 101941. https://doi.org/10.1016/j.jth.2024.101941
Wood, J. M., Henry, E., Kaye, S. A., Black, A. A., Glaser, S., Anstey, K. J., & Rakotonirainy, A. (2024). Exploring perceptions of Advanced Driver Assistance Systems (ADAS) in older drivers with age-related declines. Transportation research part F: traffic psychology and behaviour, 100, 419-430. https://doi.org/10.1016/j.trf.2023.12.006
Yang, K., Al Haddad, C., Alam, R., Brijs, T., & Antoniou, C. (2024). Adaptive intervention algorithms for advanced driver assistance systems. Safety, 10(1), 10. https://doi.org/10.3390/safety10010010
Yao, S., Yu, B., Chen, Y., Gao, K., Bao, S., & Shangguan, Q. (2025). Does road environment aesthetics influence risky driving behavior of autonomous vehicles? An evaluation on road readiness using explainable machine learning and random parameters multinomial logit with heterogeneity. Accident Analysis & Prevention, 211, 107877. https://doi.org/10.1016/j.aap.2024.107877
Yuan, M., & Xiao, Y. (2025). PMAKA-IoV: A Physical Unclonable Function (PUF)-Based Multi-Factor Authentication and Key Agreement Protocol for Internet of Vehicles. Information, 16(5), 404. https://doi.org/10.3390/info16050404
Zeng, M., Hashim, M. S. M., Ayob, M. N., Ismail, A. H., & Zang, Q. (2025). Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication. PeerJ Computer Science, 11, e2846. https://doi.org/10.7717/peerj-cs.2846
Zhao, W., Gong, S., Zhao, D., Liu, F., Sze, N. N., Quddus, M., & Huang, H. (2024). Developing a new integrated advanced driver assistance system in a connected vehicle environment. Expert Systems with Applications, 238, 121733. https://doi.org/10.1016/j.eswa.2023.121733
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Derechos de autor 2026 Morales para el autor y los de publicación y los demás para el autor