Browsing by Author "Pérez, Joshué"
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Item Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection: An Approach for Attention Level Detection(Springer, 2020-10-27) Vaca-Recalde, Myriam E.; Pérez, Joshué; Echanobe, Javier; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; CCAMDrivers provide a wide range of focus characteristics that can evaluate their attention level and analyze their behavioral states while driving. This information is critical for the development of new automated driving functionalities that support and assist the driver according to his/her state, ensuring safety for them and other users on the road. In this sense, this paper proposes a Driver Monitoring System (DMS) based on image processing and Convolutional Neural Networks (CNN), that analyzes two important driver distraction aspects: inattention of the road and drowsiness. Our approach makes use of CNN models for detecting the gaze and the head direction, which involves training datasets with different pre-defined labels. Additionally, the system is complemented with the drowsiness level measurement, using face features to detect the time that the eyes are closed or opened, and the blinking rate. Crossing the inference results of these models, the system can provide an accurate estimation of driver attention level. The different parts of the presented DMS have been trained in a Hardware-in-the-loop driving simulator with an eye fish camera. It has been tested as a real-time application recording driver with different characteristics.Item An energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimator(2021-01-13) Parra, Alberto; Zubizarreta, Asier; Pérez, Joshué; Tecnalia Research & Innovation; CCAMIn electric vehicles (EVs) with multiple motors, torque vectoring (TV) control can effectively enhance the cornering response and safety. Moreover, TV systems can also improve the overall efficiency through an optimal torque distribution that also considers the power consumption. For such a complex control system with multiple objectives, intelligent control techniques have demonstrated to be one of the best alternatives. However, the works proposed in the literature do not handle both vehicle dynamics behavior and energy efficiency, and generally do not consider the real-time implementability of the developed controllers. To overcome the aforementioned isues, in this work, a novel torque vectoring approach is proposed, which uses a neural network-based vertical tire forces estimator and considers the regenerative braking capabilities of EVs. Moreover, the implementability of the controller in a heterogenous (FPGA and microcontroller) automotive suitable system on chip is addressed, ensuring its real-time capabilities. For the sake of validating the proposed approach, a set of experiments have been carried out in a hardware in the loop setup. The performance of the proposed TV approach has been compared with other two TV approaches from the literature, evaluating them in several challenging manoeuvres in high and low tire-road friction coefficient scenarios. Results show that the proposed approach not only is able to enhance the vehicle dynamics behavior but also to decrease the energy consumption about 13%.Item From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework: The adaptive co-pilot as the enabler for a new cooperative framework(2021-07-28) Marcano, Mauricio; Tango, Fabio; Sarabia, Joseba; Castellano, Andrea; Pérez, Joshué; Irigoyen, Eloy; Díaz, Sergio; CCAMThe “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.Item Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors(2018) Parra, Alberto; Zubizarreta, Asier; Pérez, Joshué; Dendaluce, Martín; Tecnalia Research & Innovation; CCAMTransport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.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 On pre-emptive vehicle stability control(2021) Parra, Alberto; Tavernini, Davide; Gruber, Patrick; Sorniotti, Aldo; Zubizarreta, Asier; Pérez, Joshué; Tecnalia Research & Innovation; CCAMFuture vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview.