Teleoperation of Highly Automated Vehicles in Public Transport: User-Centered Design of a Human-Machine Interface for Remote-Operation and Its Expert Usability Evaluation

Kettwich, Carmen; Schrank, Andreas; Oehl, Michael

doi:10.3390/mti5050026

Cited by 43 publications

(26 citation statements)

References 37 publications

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“…Existing research on SAVs is gradually combined with service design to create a sense of safety and comfort for passengers, (Kettwich et al, 2021) such as providing accurate travel information, remote control in emergencies, and recreational functions during the journey. This emerging field uses qualitative analysis methods such as interviews, focus groups, surveys, and field studies to target a broader range of users to provide equitable and inclusive opportunities.…”

Section: Collaborative Construction Of Hmi Inside the Savsmentioning

confidence: 99%

Implications of Human-Machine Interface for Inclusive Shared Autonomous Vehicles

Yan¹,

Rampino²,

Zhao³

et al. 2022

Human Factors in Transportation

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Autonomous Vehicles (AVs), also known as self-driving cars, driverless cars, or robot cars, can perceive their environment and drive safely with little or no human inputs. Under the support of engineering, information science, anthropology, AVs have developed rapidly at the technical level, demonstrating to reduce human error operations and lessening road usage to save time. In the future, users will always be more released from driving tasks Self-driving vehicles can be more advantageous and feasible in public transportation than in private ones. Therefore, shared buses and logistics vehicles have been trial operation in various countries nowadays. Moreover, this technology will enable road traffic for people who cannot drive due to various physical and cognitive impairments. Therefore, inclusivity is often cited as one of the main reasons for promoting user acceptance of this technology, and it is fundamental in its application. Inclusive design can be achieved by identifying and addressing as many barriers to the Human-Machine Interface (HMI) as possible, focusing on human factors inside and outside the self-driving vehicle. It can enable groups with different needs to interact comfortably with the AVs and the traffic environment. Specifically, within a shared autonomous vehicle, we focus on transferring vehicle control between multiple users, reducing human error, and improving system availability. In addition, for many non-driving-related tasks (NDRT) derived from the gradual improvement of autonomous driving, the design of interactive devices and processes suitable for different groups and their cognition, the usability and comfort of the system will be improved, as well as the user experience. Concerning the scenes outside the vehicle, the impact of different types of external HMIs on the user experience of other user groups (e.g., young, old, cognitive, or physical disabilities, Etc.) attracted more attention from scholars. At the same time, communication channels and interfaces established between users and AVs will become more prominent on an inclusive basis. Consequently, HMI is essential for functional and inclusive driving automation, guaranteeing an efficient and satisfactory interaction between the automated system and different users.This paper provides an overview of the HMI challenges in shared driving automation from an inclusive design perspective, summarizing existing research on the role of HMIs in shared autonomous vehicles. The authors identified the fundamental changes in the way the user interacts with the car in shared autonomous vehicles using a systematic literature review including the following four steps: 1) identification of purposes and research questions of the literature review 2) definition of a literature search strategy by identifying a combination of sequential and iterative search queries; 3) analyze the retrieved articles compiling a concept matrix for each of them. As a result, relevant literature related to the research topic was selected; 4) identifying research gaps and inconsistent research results to make tacit domain meta-knowledge explicit. The paper will contain the analysis and discussion of the obtained data. Finally, the paper will discuss the future challenges for promoting a deeper exploration of inclusivity of HMIs for autonomous vehicles, also proposing the research avenues practical to increase the user's acceptance of this technology.

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Section: Collaborative Construction Of Hmi Inside the Savsmentioning

confidence: 99%

Implications of Human-Machine Interface for Inclusive Shared Autonomous Vehicles

Yan¹,

Rampino²,

Zhao³

et al. 2022

Human Factors in Transportation

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“…Kettwich et al [28] use the terms teleoperation and remote-operation / control interchangeably. They separate direct and indirect approaches, describing the latter as human interventions.…”

Section: Research Approachesmentioning

confidence: 99%

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Bogdoll,

Orf,

Töttel

et al. 2021

Preprint

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Corner cases for driving automation systems can often be detected by the system itself and subsequently resolved by remote humans. There exists a wide variety of approaches on how remote humans can resolve such issues. Over multiple domains, no common taxonomy on those approaches has developed yet. As the scaling of automated driving systems continues to increase, a uniform taxonomy is desirable to improve communication within the scientific community, but also beyond to policymakers and the general public. In this paper, we provide a survey on recent terminologies and propose a taxonomy for remote human input systems, classifying the different approaches based on their complexity.

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“…The advent of autonomous vehicles is likely to transform individual and public transportation [ 1 ] as well as freight transport and intralogistics [ 2 , 3 ]. Autonomous vehicles may perform well in the scenarios above, but they may still struggle in critical traffic circumstances that a human could easily handle, such as parking lots, pedestrian crossing areas, or construction roads.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Teleoperation: Human in the Loop Performance Comparison of Smith Predictor with Novel Successive Reference-Pose Tracking Approach

Prakash

Vignati

Sabbioni

et al. 2022

Sensors

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Vehicle teleoperation has the ability to bridge the gap between completely automated driving and manual driving by remotely monitoring and operating autonomous vehicles when their automation fails. Among many challenges related to vehicle teleoperation, the considered ones in this work are variable time delay, saturation of actuators installed in vehicle, and environmental disturbance, which together limit the teleoperation performance. State-of-the-art predictive techniques estimate vehicle states to compensate for the delays, but the predictive states do not account for sudden disturbances that the vehicle observes, which makes the human-picked steer inadequate. This inadequacy of steer deteriorates the path-tracking performance of vehicle teleoperation. In the proposed successive reference-pose-tracking (SRPT) approach, instead of transmitting steering commands, the reference trajectory, in the form of successive reference poses, is transmitted to the vehicle. This paper introduces a method of generation of successive reference poses with a joystick steering wheel and compares the human-in-loop path-tracking performance of the Smith predictor and SRPT approach. Human-in-loop experiments (with 18 different drivers) are conducted using a simulation environment that consists of the integration of a real-time 14-DOF Simulink vehicle model and Unity game engine in the presence of bidirectional variable delays. Scenarios for performance comparison are low adhesion ground, strong lateral wind, tight corners, and sudden obstacle avoidance. Result shows significant improvement in reference tracking and in reducing human effort in all scenarios using the SRPT approach.

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Teleoperation of Highly Automated Vehicles in Public Transport: User-Centered Design of a Human-Machine Interface for Remote-Operation and Its Expert Usability Evaluation

Cited by 43 publications

References 37 publications

Implications of Human-Machine Interface for Inclusive Shared Autonomous Vehicles

Implications of Human-Machine Interface for Inclusive Shared Autonomous Vehicles

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Vehicle Teleoperation: Human in the Loop Performance Comparison of Smith Predictor with Novel Successive Reference-Pose Tracking Approach

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