Online Feedback Control for Driver-Vehicle Interaction in Automated Driving

Nobari, Khazar Dargahi; Albers, Franz; Bartsch, Katharina; Bertram, Torsten

doi:10.1007/978-3-030-50943-9_21

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The proposed system consists of elements such as sensors embedded in the car, an adaptive inference engine that analyzes the driver-vehicle interaction, and an advanced digital platform in the vehicle cabin, which accesses the data obtained from this interaction. Dargahi Nobari et al [111] propose a control scheme with feedback that considers the state of the driver as an input element for the system that analyzes the driver-vehicle interaction. Sensors (e.g., eye-tracker, physiological sensors) are used to detect the state of the driver.…”

Section: Actual Statementioning

confidence: 99%

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

González-Saavedra

Figueroa

Céspedes

et al. 2022

Sensors

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The design of cooperative advanced driver assistance systems (C-ADAS) involves a holistic and systemic vision that considers the bidirectional interaction among three main elements: the driver, the vehicle, and the surrounding environment. The evolution of these systems reflects this need. In this work, we present a survey of C-ADAS and describe a conceptual architecture that includes the driver, vehicle, and environment and their bidirectional interactions. We address the remote operation of this C-ADAS based on the Internet of vehicles (IoV) paradigm, as well as the involved enabling technologies. We describe the state of the art and the research challenges present in the development of C-ADAS. Finally, to quantify the performance of C-ADAS, we describe the principal evaluation mechanisms and performance metrics employed in these systems.

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Section: Actual Statementioning

confidence: 99%

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

González-Saavedra

Figueroa

Céspedes

et al. 2022

Sensors

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“…Unlike unexpected, non-scheduled takeovers (e.g., emergency brake of a lead vehicle in congestion) in which drivers must respond within a short time window, the takeover events due to ODD exit such as exiting a freeway can be scheduled ahead of time rendering drivers ample time to perform a takeover task (McCall et al, 2019). Previous empirical studies adopted a ToR lead time ranging from 6 to 60 seconds (s) for freeway exiting takeovers (Holländer & Pfleging, 2018; Nobari et al, 2020; Petermeijer et al, 2017; Tan & Zhang, 2022; Wörle et al, 2020; Yun & Yang, 2020). These studies were mostly focused on takeover performance (such as takeover response time and vehicle control) and subjective experience.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Driver Strategy for Scheduled Takeovers Under the Effect of Multi-stage Takeover Request Design

Tan,

Zhang

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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For scheduled takeover events in conditional driving automation, a longer lead time of takeover request (ToR) up to 60 seconds was recommended for drivers’ better situation awareness and subjective evaluations. However, the long ToR lead time could impair drivers’ estimation of the urgency of situation, which potentially results in risky takeover behavior. Four multi-stage ToR designs varying in warning modality that communicate the urgency within a long lead time were thus proposed to mitigate the negative effects. The present study involved a driving simulator experiment with 32 participants performing 12 takeover tasks for exiting a freeway with the aim to analyze driver strategy for scheduled takeover events and evaluate the multi-stage ToR designs. Eight driver strategies for attention management and takeover were identified. Using multi-stage ToR designs advanced drivers’ takeover actions by decreasing the likelihood of alternating attention between non-driving activities and road and reducing the duration of takeover preparation.

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Modeling driver-vehicle interaction in automated driving

Nobari

Albers

Bartsch

et al. 2022

Forsch Ingenieurwes

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In automated vehicles, the collaboration of human drivers and automated systems plays a decisive role in road safety, driver comfort, and acceptance of automated vehicles. A successful interaction requires a precise interpretation and investigation of all influencing factors such as driver state, system state, and surroundings (e.g., traffic, weather). This contribution discusses the detailed structure of the driver-vehicle interaction, which takes into account the driving situation and the driver state to improve driver performance. The interaction rules are derived from a controller that is fed by the driver state within a loop. The regulation of the driver state continues until the target state is reached or the criticality of the situation is resolved. In addition, a driver model is proposed that represents the driver’s decision-making process during the interaction between driver and vehicle and during the transition of driving tasks. The model includes the sensory perception process, decision-making, and motor response. The decision-making process during the interaction deals with the cognitive and emotional states of the driver. Based on the proposed driver-vehicle interaction loop and the driver model, an experiment with 38 participants is performed in a driving simulator to investigate (1) if both emotional and cognitive states become active during the decision-making process and (2) what the temporal sequence of the processes is. Finally, the evidence gathered from the experiment is analyzed. The results are consistent with the suggested driver model in terms of the cognitive and emotional state of the driver during the mode change from automated system to the human driver.

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Online Feedback Control for Driver-Vehicle Interaction in Automated Driving

Cited by 7 publications

References 9 publications

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision

Analyzing Driver Strategy for Scheduled Takeovers Under the Effect of Multi-stage Takeover Request Design

Modeling driver-vehicle interaction in automated driving

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