Browsing by Author "Martí, Enrique"
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Item Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms(2018) Marcano, Mauricio; Matute, José A.; Lattarulo, Ray; Martí, Enrique; Pérez, Joshué; CCAM; Tecnalia Research & InnovationAdvanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution.Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.Item Towards Risk Estimation in Automated Vehicles Using Fuzzy Logic(Springer Verlag, 2018) González, Leonardo; Martí, Enrique; Calvo, Isidro; Ruiz, Alejandra; Pérez, Joshue; Bitsch, Friedemann; Skavhaug, Amund; Gallina, Barbara; Schoitsch, Erwin; CCAM; Tecnalia Research & Innovation; QuantumAs vehicles get increasingly automated, they need to properly evaluate different situations and assess threats at run-time. In this scenario automated vehicles should be able to evaluate risks regarding a dynamic environment in order to take proper decisions and modulate their driving behavior accordingly. In order to avoid collisions, in this work we propose a risk estimator based on fuzzy logic which accounts for risk indicators regarding (1) the state of the driver, (2) the behavior of other vehicles and (3) the weather conditions. A scenario with two vehicles in a car-following situation was analyzed, where the main concern is to avoid rear-end collisions. The goal of the presented approach is to effectively estimate critical states and properly assess risk, based on the indicators chosen.Item Urban Motion Planning Framework Based on N-Bézier Curves Considering Comfort and Safety(2018) Lattarulo, Ray; González, Leonardo; Martí, Enrique; Matute, José; Marcano, Mauricio; Pérez, Joshue; CCAM; Tecnalia Research & InnovationIn last decades, great technology advances have been done related to the automotive sector, especially in Advanced Driver Assistance Systems (ADAS) developed to improve mobility in terms of comfort and safety during driving process; hence, automated driving is presented as an evolution of those systems in the present and upcoming years. The aim of this work is to present a complete framework of motion planning for automated vehicles, considering different constraints with parametric curves for lateral and longitudinal planners. Parametric Bézier curves are used as the core approach for trajectory design in intersections, roundabouts, and lane change maneuvers. Additionally, a speed planner algorithm is presented using the same parametric curve approach, considering comfort and safety. A simulation environment is used for testing the planning method in urban conditions. Finally, tests with the real platform in automated mode have been performed showing goods results.