Browsing by Author "Villagra, Jorge"
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Item Human-machine interaction(Elsevier, 2023-01-01) Marcano, Mauricio; Villagra, Jorge; Medina-Lee, Juan; Pérez, Joshué; Diaz, Sergio; CCAMResearchers and automakers are working toward more intelligent and robust ADAS to develop fully automated vehicles. However, they have been facing a hard challenge because of the complexity of the scenarios a driver faces every day. In that order, automated cars must assure almost perfect performance because human-caused accidents are socially and legally accepted, but those caused by machines are not. In this context, two main Human-Machine Cooperation (HMC) strategies are being explored to overcome the main challenges of fully autonomous vehicles (AVs) and propose new solutions that can be implemented in the short term to improve safety and efficiency of the driving task. These strategies are shared control and traded control. The first emphasizes the real-time cooperation at the control level between the driver and automation, with a dynamic allocation of control authority. The second looks for a dynamic shift of the human role between the driver and passenger, with a variable level of automation according to the complexity of the driving scenario. The present chapter provides a detailed description of both strategies with recent developments in terms of frameworks and algorithms.Item Motion planning(Elsevier, 2023-01-01) Villagra, Jorge; Jiménez, Felipe; Pérez, Joshué; Garcia-Daza, Ivan; Artuñedo, Antonio; Clavijo, Miguel; Díaz-Álvarez, Alberto; Fernandez-Lorca, David; Lattarulo, Ray Alejandro; Matute, Jose Ángel; Godoy, Jorge; Izquierdo-Gonzalo, Rubén; Alonso, Marta; CCAMMotion planning is responsible for computing a safe, comfortable, and dynamically feasible trajectory from the automated vehicle's current state to the goal configuration provided by the behavioral layer of the decision-making system. It considers information about static and dynamic obstacles around the vehicle and generates a collision-free trajectory that satisfies dynamic and kinematic constraints on the motion of the vehicle. This chapter defines the problem and provides a complete overview of the different existing techniques to obtain (i) appropriate paths along the drivable space; and (ii) the most suitable speed profile to follow that path. Although path planning is a well-established discipline in robotics, the focus of the chapter is on describing the different approaches under the perspective, needs, and constraints of on-road vehicles.Item Programmable systems for intelligence in automobiles (PRYSTINE): Final results after Year 3(Institute of Electrical and Electronics Engineers Inc., 2021) Druml, Norbert; Ryabokon, Anna; Schorn, Rupert; Koszescha, Jochen; Ozols, Kaspars; Levinskis, Aleksandrs; Novickis, Rihards; Nigussie, Ethiopia; Isoaho, Jouni; Solmaz, Selim; Stettinger, Georg; Diaz, Sergio; Marcano, Mauricio; Villagra, Jorge; Medina, Juan; Schwarz, Martina; Artuñedo, Antonio; Comi, Mauro; Beekelaar, Rutger; Özçelik, Onur; Taşdelen, Elif Aksu; Gürbüz, Yeşim; Saijets, Jan; Kyynäräinen, Jukka; Morits, Dmitry; Debaillie, Björn; Rykunov, Maxim; Escamilla, Joan; Vanne, Jarno; Korhonen, Tomi; Holma, Kalle; Matzhold, Eva Maria; Novara, Carlo; Tango, Fabio; Burgio, Paolo; Calafiore, Giuseppe; Karimshoushtari, Milad; Boulay, Emilie; Dhaens, Miguel; Praet, Kylian; Zwijnenberg, Han; Palm, Henri; Ortega, David Aledo; Kalali, Ercan; Pensala, Tuomas; Kyytinen, Arto; Larsen, Morten; Veledar, Omar; Macher, Georg; Lafer, Michael; Giraudi, Lorenzo; Reckenzaun, Jakob; Hammer, Daniel; Mohan, Naveen; Schmid, Josef; Höß, Alfred; Ophir, Shai; Dubey, Anand; Fuchs, Jonas; Lübke, Maximilian; Anghel, Andrei; Ristea, Nicolae Cătălin; Törngren, Martin; Musralina, Alua; Harter, Marlene; Jose, Joseena Memadathil; Dimitrakopoulos, George; Leporati, Francesco; Vitabile, Salvatore; Skavhaug, Amund; CCAM; Tecnalia Research & InnovationAutonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches. The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.