2026-06-08T00:21:15Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/1299262026-01-13T00:49:16Zcom_20.500.14352_14col_20.500.14352_21
Docta Complutense author Pérez Moreno, Elisa María author Jiménez, Felipe author Naranjo, José Eugenio author Palomares, Nicolás author Silva, Javier author Laparra-Hernández, José author Solaz, José editor Villagra, Jorge editor Jiménez, Felipe 2026-01-12T15:45:32Z 2026-01-12T15:45:32Z 2023 Pérez-Moreno, E., Jiménez, F., Naranjo, J.E., Palomares, N., Silva, J., Laparra-Hernández, J. & Solaz, J. (2023). Driver Behavior. In J. Villagra & F. Jiménez (Eds.), Decision-Making Techniques for Autonomous Vehicles (283-332). Elsevier. 978-0-323-98339-6 10.1016/C2021-0-00579-4 https://hdl.handle.net/20.500.14352/129926 https://doi.org/10.1016/B978-0-323-98339-6.00007-5 https://doi.org/10.1016/C2021-0-00579-4https://www.sciencedirect.com/book/edited-volume/9780323983396/decision-making-techniques-for-autonomous-vehicles#table-of-contents This chapter examines driver behavior within automated driving systems from a human-centered perspective, emphasizing the integration of cognitive, behavioral, and decision-making processes into the design and evaluation of vehicle automation. First, it outlines the theoretical foundations of human–automation interaction (HAI), highlighting the limitations of purely technology-driven approaches and the need to conceptualize the driver as an integral and active component of the control loop. Particular attention is given to intermediate levels of automation, where drivers are required to supervise the system and resume control under time-critical conditions.The chapter focuses on the assessment of driver state within HAI models, identifying key dimensions influenced by automation, including mental workload, situational awareness, automation complacency, and skill degradation. It analyzes how automated driving may induce both cognitive underload and overload, leading to reduced vigilance and impaired takeover performance during transitions of control. A comprehensive review of subjective, behavioral, and physiological assessment methods is provided, underscoring the advantages of multimodal and non-intrusive approaches for real-time driver monitoring. Overall, the chapter argues that safe and effective automated driving requires adaptive systems capable of continuously evaluating driver state and supporting robust human–vehicle cooperation to mitigate automation-related performance degradation. eng Driver behavior book part 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URL https://docta.ucm.es/bitstreams/36b711bc-03ac-4b39-859f-f17494231e6f/download File MD5 92cc2e8d4a6ca6f1fa71c7fbdebd5e2d 978252 application/pdf Driver Behavior in D-MTAV.pdf