Arturo Tejada Ruiz
Contacta.tejada.ruiz@ tue.nl Gemini zuid 0.131
Arturo Tejada is Part-time Assistant Professor of Safe Autonomous and Cooperative Vehicles at the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). His work focuses on "teaching" self-driving vehicles how to safely and socially interact with human drivers. This is done by developing reference models of human driving from which design requirements for automated driving functions can be can be extracted and demonstrated. His work sits at the intersection of human factors, artificial intelligence and motion control of automated vehicles.
Driving is a form of social interation mediated by technology (i.e., our vehicles). Consequently, self-driving vehicles need to be socialized, the same way we socialize our dogs: we need to teach them how to behave, so that they can interact with us as we expect.
Arturo Tejada holds Bachelor degree in Electrical Engineering from the Pontificia Universidad Católica del Perú and MSc. and Ph. D. degrees in Electrical Engineering from Old Dominion University in Norfolk, Virginia (2006), where he specialized in hybrid system theory. He is the author of over 40 scientific publications in top peer-reviewed conferences and journals. In addition to his role as Assistant Professor, he is also a Senior Scientist at the Integrated Vehicle Safety department of TNO, where he supports OEMs and regulators in certifying and monitoring the safe behavior of automated vehicles in traffic.
Towards a Characterization of Safe Driving Behavior for Automated Vehicles Based on Models of "Typical" Human Driving Behavior23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 (2020)
Spatial anomaly detection in sensor networks using neighborhood informationInformation Fusion (2017)
Towards observer-based fault detection and isolation for branched water distribution networks without cycles14th European Control Conference, ECC 2015 (2015)
Towards WaterLab1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks, CySWater 2015 (2015)
Ensembles of incremental learners to detect anomalies in ad hoc sensor networksAd Hoc Networks (2015)
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