Rolf Boink

Bio: Rolf Boink is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Haptic technology. The author has an hindex of 2, co-authored 3 publications receiving 43 citations.

Understanding and reducing conflicts between driver and haptic shared control

Abstract: Haptic shared control systems aim to guide drivers during steering using guidance torques. Many such systems generate torques using a simplified and constant lane-keeping model based on a look-ahead controller, without accounting for individual differences. Literature on haptic steering support shows beneficial effects (reduced control activity and increased performance) under experimental conditions, but also report increased steering torques. In this study, we hypothesized that increased interacting torques are the results of small conflicts between human and a constant haptic support system and that these conflicts may be mitigated by adapting the parameters of the look-ahead controller to best match each individual driver, essentially providing individualized guidance torques. Results showed that this approach provides a better match in terms of desired steering wheel angles, however this did not lead to a relevant reduction in steering torques, as discrepancies in timing and lateral error continued to exist. Future adaptations to the haptic shared control algorithm should take more realistic driver control models into consideration.

A Topology of Shared Control Systems—Finding Common Ground in Diversity

Abstract: Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain–machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human–automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.

A Review of Shared Control for Automated Vehicles: Theory and Applications

Abstract: The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach

Abstract: The challenging issue of “human–machine copilot” opens up a new frontier to enhancing driving safety. However, driver–machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver–vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver–IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.

The Effect of Haptic Support Systems on Driver Performance: A Literature Survey

Abstract: A large number of haptic driver support systems have been described in the scientific literature. However, there is little consensus regarding the design, evaluation methods, and effectiveness of these systems. This literature survey aimed to investigate: (1) what haptic systems (in terms of function, haptic signal, channel, and supported task) have been experimentally tested, (2) how these haptic systems have been evaluated, and (3) their reported effects on driver performance and behaviour. We reviewed empirical research in which participants had to drive a vehicle in a real or simulated environment, were able to control the heading and/or speed of the vehicle, and a haptic signal was provided to them. The results indicated that a clear distinction can be made between warning systems (using vibrations) and guidance systems (using continuous forces). Studies typically used reaction time measures for evaluating warning systems and vehicle-centred performance measures for evaluating guidance systems. In general, haptic warning systems reduced the reaction time of a driver compared to no warnings, although these systems may cause annoyance. Guidance systems generally improved the performance of drivers compared to non-aided driving, but these systems may suffer from after-effects. Longitudinal research is needed to investigate the transfer and retention of effects caused by haptic support systems.

Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving

Abstract: The last decade witnessed a great development of automated driving vehicles (ADVs) and vehicle intelligence. The significant increment of machine intelligence poses a new challenge to the community, which is the collaboration between human drivers and vehicle autonomy. As vehicle autonomy is gaining more control authority, the roles that human drivers can play in the future need to be further clarified. In this study, literature review and perspectives on the human behaviors and cognition (HBC) for ADVs toward human-autonomy (H-A) collaboration are proposed. First, the H-A collaboration basics and key factors are reviewed. Then, the HBC issues in driver behavior modeling and understanding are discussed. Specifically, two key factors are reviewed, which are human trust and situation awareness (SA). Next, HBC in two H-A collaboration-enabled vehicle control methods, namely, shared control and take-over control, are analyzed. It is shown that the human-centered collaboration strategy that integrates driver states will largely increase the acceptance of the ADVs. Then, the HBC issues in the design of human-machine-interface (HMI) for future autonomous and collaboration-enabled ADVs are discussed. Last, challenges and future works for H-A collaboration on ADVs are analyzed to contribute to the development of understandable, trustable, and acceptable ADVs.