Human-machine interaction (HMI) is about the interaction design inside the cockpit of vehicle. It takes the human-centered design approach related to systems that help driver or passengers’ communication with vehicles. Ensuring safer driving and more effective communication between the human and the vehicle is the central focus. Its design challenges have changed a lot due to the increasing level of autonomous driving. What kind of interaction will be the mainstream? How would a driver’s personality influence the driver’s attitude toward active safety and trust of the autonomous driving? How should HMI be designed for the handover scenarios from Level 3 to Level 5 autonomous driving? What HMI solutions make the driving safer and improve user experience?

This special issue includes eight papers. The first five papers seek to address the above questions by assessing the current approaches and technologies, as well as to outline the major challenges and future perspectives related to HMI technology applied in intelligent vehicles. The other three papers are related to autonomous driving.

We hope this special issue could motivate research in the vehicle HMI design and promote the building of quality criteria for automotive user interfaces and autonomous driving systems in the automotive industry.

We would first like to gratefully acknowledge and sincerely thank all the reviewers for their timely and insightful valuable comments and criticism of the manuscripts that greatly improved the quality of the final versions. Of course, thanks are due to the authors, who provided excellent papers and timely extended revisions. Finally, we are grateful to the editors of Automotive Innovation for their trust in us, their efforts, patience, and painstaking editorial work during the production of this special issue.

Maintaining an appropriate level of trust in automated driving (AD) is critical to safe driving. However, few studies have explored factors affecting trust in AD in general, and no study, as far as is known, has directly investigated whether driver personality influences driver trust in an AD system. The current study investigates the relation between driver personality and driver trust in AD, focusing on Level 2 AD. Participants were required to perform a period of AD in a driving simulator, during which their gaze and driving behavior were recorded, as well as their subjective trust scores after driving. In three distinct measures, a significant correlation between Openness and driver trust in the AD system is found: participants with higher Openness traits tend to have less trust in the AD system. No significant correlations between driver trust in AD and other personality traits are found. The findings suggest that driver personality has an impact on driver trust in AD. Theoretical and practical implications of this finding are discussed.

Conditionally and highly automated vehicles will require drivers to take control as a result of a non-emergency, such as a geographical, terrain, capability or design boundary. It is anticipated that these events will provide the driver with a sufficient amount of time to prepare themselves for the transition of control. This study explores conditionally and highly automated vehicle transitions of control by asking how drivers of differing skill levels (learner, intermediate and advanced) approach the task of designing an interface responsible for making transitions safer, more usable and more efficient. Three focus groups generated detailed designs for vehicle-to-driver transitions in an 1-h and a 10-min “out-of-the-loop” scenarios and transitions from driver to vehicle. Results show great variation in the approaches taken by each skill group (e.g., the reliance on visual interfaces for awareness assist and viewpoints on issues such as multimodal displays). Customization was a common theme throughout, with drivers desiring the option to adjust alert timings and modalities in which information is displayed. This paper presents these designs along with a detailed comparison of group designs and implements distributed situation awareness theory to discuss findings and draw conclusions.

The design with intent (DwI) toolkit assists designers in creating novel designs and interfaces. DwI, however, is not constrained to any degree, making it impossible to know whether the produced designs adequately account for users’ needs. In contrast, cognitive work analysis (CWA) is a human factors research tool that seeks to map a system and account for users’ needs, yet does not provide clear guidelines for progressing such analysis into workable designs with which users can interact. This paper seeks to present a proof-of-concept investigation to demonstrate that DwI can be suitably constrained and validated by insights gained from CWA. CWA, in turn, benefits by having a suitable toolkit for progressing insights. Two teams of individuals without design backgrounds were able to develop mock-up in-vehicle interfaces aimed at reducing fuel use. The teams were able to use DwI toolkit to articulate the genesis of their ideas, which in turn could be directly linked to system needs identified within CWA.

Introduction

In order to assess the usability of steering wheel control interfaces in motorsport, it is necessary to employ a set of appropriate methods. The method selection process first involves identifying the relevant motorsport-based usability criteria. The unique factors associated with context of use in motorsport are then defined. These are employed to synthesize a set of key performance indicators (KPIs) that require specific analysis. The first KPI relates to error rates; these should be minimized, particularly under high cognitive load on the primary. The second KPI involves task times; these should be minimized not just to reduce distraction, but also to improve competitive performance. The third KPI states that usability should be optimized to minimize visual distraction. The fourth KPI advocates the minimizing of interface task load to reduce the effect on the primary task. The final KPI states that interface functionality should be easy to learn and recall. The KPIs provide clear goals to guide the identification of the most appropriate methods for each aspect of usability. Methods categories are devised and sets of appropriate methods selected based on the KPIs. These then combine into a toolset with an associated application process with the overall goal of providing a means by which motorsport interfaces may be both analysed and improved.

Technological developments in the domain of vehicle automation are targeted toward driver-less, or driver-out-of-the-loop driving. The main societal motivation for this ambition is that the majority of (fatal) accidents with manually driven vehicles are due to human error. However, when interacting with technology, users often experience the need to customize the technology to their personal preferences. This paper considers how this might apply to vehicle automation, by a conceptual analysis of relevant use cases. The analysis proceeds by comparing how handling of relevant situations is likely to differ between manual driving and automated driving. The results of the analysis indicate that full out-of-the-loop automated driving may not be acceptable to users of the technology. It is concluded that a technology that allows shared control between the vehicle and the user should be pursued. Furthermore, implications of this view are explored for the concrete temporal dynamics of shared control, and general characteristics of human machine interface that support shared control are proposed. Finally, implications of the proposed view and directions for further research are discussed.

In active collision avoidance, the trajectory tracking controller determines the deviation from the reference path and the vehicle stability. The main objective of this study was to reduce the tracking error and improve the tracking performance in collision avoidance. Unlike the previously proposed model predictive control (MPC) strategies with constant sampling time, an improved MPC controller with varying sampling time based on the hierarchical control framework was proposed in this paper. Compared with the original MPC tracking controller, the improved MPC controller demonstrated better adaptive capability for the varying road adhesion coefficients and vehicle speed on a curved road. The simulation results revealed that the hierarchical control framework generated an optimal trajectory for collision avoidance in real-time by minimizing the potential field energy.

Most current research on the trajectory planning of the autonomous lane change focuses on high-speed scenarios and assumes that the states of the surrounding vehicles keep stable during the lane change. The methods based on geometric-curve are mostly used for trajectory planning. In this paper, considering the inevitable development of the autonomous driving, the surrounding vehicles are assumed to be driven by human drivers, while the ego vehicles are able to autonomously change lanes. Representative local lane-change scenarios are then designed and analyzed in detail aiming at medium- and low-speed lane-change conditions. Additionally, in contrast with most research, dynamic trajectory planning which considers the possible state variations of the surrounding vehicles and the driver characteristics is studied and described by a fifth-order polynomial function. The safety and comfort of the dynamic trajectory planning are validated through simulation. Moreover, the elastic soft constraint of the safety domain is designed, whereby the sensitivity of the studied dynamic trajectory planning system is reduced under the premise of ensuring safety. The effectiveness of the elastic soft constraint in terms of improving comfort during the lane change is verified through simulation. The availability of the dynamic trajectory planning system with the elastic soft constraint is demonstrated with the addition of trajectory tracking based on model predictive control, showing its potential in practical applications.

This paper proposes a robust cooperative control strategy for multiple autonomous vehicles to achieve safe and efficient platoon formation, and it analyzes the effects of vehicle stability boundaries and parameter uncertainties. The cooperative vehicle control framework is composed of the upper planning level and lower tracking control level. In the planning level, the trajectory of each vehicle is generated by using the multi-objective flocking algorithm to form the platoon. The parameters of the flocking algorithm are optimized to prevent the vehicle speed and yaw rate from going beyond their limits. In the lower level, to realize the stable platoon formation, a lumped disturbance observer is designed to gain the stable-state reference, and a distributed robust model predictive controller is proposed to achieve the offset-free trajectory tracking while downsizing the effects of parameter uncertainties. The simulation results show the proposed cooperative control strategy can achieve safe and efficient platoon formation.