Modelling communication-enabled traffic interactions.

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Shared control versus traded control in driving: a debate around automation pitfalls.

A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections-which have evolved through classroom debates between the authors over the past 10 years-on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively. Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design. Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed.

Does haptic steering guidance instigate speeding? A driving simulator study into causes and remedies.

An important issue in road traffic safety is that drivers show adverse behavioral adaptation (BA) to driver assistance systems. Haptic steering guidance is an upcoming assistance system which facilitates lane-keeping performance while keeping drivers in the loop, and which may be particularly prone to BA. Thus far, experiments on haptic steering guidance have measured driver performance while the vehicle speed was kept constant. The aim of the present driving simulator study was to examine whether haptic steering guidance causes BA in the form of speeding, and to evaluate two types of haptic steering guidance designed not to suffer from BA. Twenty-four participants drove a 1.8m wide car for 13.9km on a curved road, with cones demarcating a single 2.2m narrow lane. Participants completed four conditions in a counterbalanced design: no guidance (Manual), continuous haptic guidance (Cont), continuous guidance that linearly reduced feedback gains from full guidance at 125km/h towards manual control at 130km/h and above (ContRF), and haptic guidance provided only when the predicted lateral position was outside a lateral bandwidth (Band). Participants were familiarized with each condition prior to the experimental runs and were instructed to drive as they normally would while minimizing the number of cone hits. Compared to Manual, the Cont condition yielded a significantly higher driving speed (on average by 7km/h), whereas ContRF and Band did not. All three guidance conditions yielded better lane-keeping performance than Manual, whereas Cont and ContRF yielded lower self-reported workload than Manual. In conclusion, continuous steering guidance entices drivers to increase their speed, thereby diminishing its potential safety benefits. It is possible to prevent BA while retaining safety benefits by making a design adjustment either in lateral (Band) or in longitudinal (ContRF) direction.

The impact of haptic feedback quality on the performance of teleoperated assembly tasks.

In teleoperation, haptic feedback allows the human operator to touch the remote environment. Yet, it is only partially understood to what extent the quality of haptic feedback contributes to human-in-the-loop task performance. This paper presents a human factors experiment in which teleoperated task performance and control effort are assessed for a typical (dis-)assembly task in a hard-to-hard environment, well known to the operator. Subjects are provided with four levels of haptic feedback quality: no haptic feedback, low-frequency haptic feedback, combined low- and high-frequency haptic feedback, and the best possible-a natural spectrum of haptic feedback in a direct-controlled equivalent of the task. Four generalized fundamental subtasks are identified, namely: 1) free-space movement, 2) contact transition, 3) constrained translational, and 4) constrained rotational tasks. The results show that overall task performance and control effort are primarily improved by providing low-frequency haptic feedback (specifically by improvements in constrained translational and constrained rotational tasks), while further haptic feedback quality improvements yield only marginal performance increases and control effort decreases, even if a full natural spectrum of haptic feedback is provided.

A Method to Measure the Relationship Between Biodynamic Feedthrough and Neuromuscular Admittance.

Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions, which can result in unintentional control inputs that can substantially degrade manual control. It is known that humans can adapt the dynamics of their limbs by adjusting their neuromuscular settings, and it is likely that these adaptations have a large influence on BDFT. The goal of this paper is to present a method that can provide evidence for this hypothesis. Limb dynamics can be described by admittance, which is the causal dynamic relation between a force input and a position output. This paper presents a method to simultaneously measure BDFT and admittance in a motion-based simulator. The method was validated in an experiment. Admittance was measured by applying a force disturbance signal to the control device; BDFT was measured by applying a motion disturbance signal to the motion simulator. To allow distinguishing between the operator's responses to each disturbance signal, the perturbation signals were separated in the frequency domain. To show the impact of neuromuscular adaptation, subjects were asked to perform three different control tasks, each requiring a different setting of the neuromuscular system (NMS). Results show a dependence of BDFT on neuromuscular admittance: A change in neuromuscular admittance results in a change in BDFT dynamics. This dependence is highly relevant when studying BDFT. The data obtained with the proposed measuring method provide insight in how exactly the settings of the NMS influence the level of BDFT. This information can be used to gain fundamental knowledge on BDFT and also, for example, in the development of a canceling controller.

Haptic gas pedal feedback.

Active driver support systems either automate a control task or present warnings to drivers when their safety is seriously degraded. In a novel approach, utilising neither automation nor discrete warnings, a haptic gas pedal (accelerator) interface was developed that continuously presents car-following support information, keeping the driver in the loop. This interface was tested in a fixed-base driving simulator. Twenty-one drivers between the ages of 24 and 30 years participated in a driving experiment to investigate the effects of haptic gas pedal feedback on car-following behaviour. Results of the experiment indicate that when haptic feedback was presented to the drivers, some improvement in car-following performance was achieved, while control activity decreased. Further research is needed to investigate the effectiveness of the system in more varied driving conditions. Haptics is an under-used modality in the application of human support interfaces, which usually draw on vision or hearing. This study demonstrates how haptics can be used to create an effective driver support interface.