Browsing by Keyword "Shared control"
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Item Development of a Driver-State Adaptive Co-Driver as Enabler for Shared Control and Arbitration(Springer Science and Business Media Deutschland GmbH, 2020) Castellano, Andrea; Carbonara, Giuseppe; Diaz, Sergio; Marcano, Mauricio; Tango, Fabio; Montanari, Roberto; Stephanidis, Constantine; Antona, Margherita; Ntoa, Stavroula; CCAMFor automated and partially automated cars, there are new crucial questions to answer: “When should the driver or the automated system take control of the vehicle?" ; and also: “Can both control the vehicle together at the same time, or can this create potential conflicts?". These are non-trivial issues because they depend on different conditions, such as the environment, driver’s state, vehicle capabilities, and fault tolerance, among others. This paper will describe a human-machine cooperation approach for collaborative driving maneuvers, developed in the EU funded project PRYSTINE. In particular, this study presents the work-in-progress and will focus attention on the proposed architecture design and the corresponding use case for testing.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 Human-automation interaction through shared and traded control applications(Springer, 2020) Marcano, Mauricio; Díaz, Sergio; Pérez, Joshue; Castellano, Andrea; Landini, Elisa; Tango, Fabio; Burgio, Paolo; Ahram, Tareq; Karwowski, Waldemar; Vergnano, Alberto; Leali, Francesco; Taiar, Redha; CCAMAutomated and Highly-automated Vehicles still need to interact with the driver at different cognitive levels. Those who are SAE Level 1 or 2 consider the human in the loop all the time and require strong participation of the driver at the control level. To increase safety, trust and comfort of the driver with this kind of automation, systems with a strong cooperative component are needed. This paper introduces the design of a vehicle controller based on shared control, together with an arbitration system, and the design of a Human-Machine Interface (HMI) to foster the mutual understanding between driver and automation in a lane-keeping task. The driver-automation cooperation is achieved through incremental support, in a continuum spectrum from manual to full automation. Additionally, the design of an HMI to support the driver in a takeover maneuver is presented. This functionality is a key component of vehicles SAE Level 3 and 4.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.