The introduction of advanced driver assistance systems (ADAS) is a significant step that establishes a level of trust between the driver and the vehicle [1,2,3]. Importantly, ADAS is to provide the driver with aid in a variety of different ways and to potentially contribute to the safety of the driving environment. In this sense, ADAS is participating in the driving operations by informing and warning the driver about extraneous chores that need to be accomplished. The development of ADAS has brought about improvements in passenger comfort and the ability to personalize their features [4,5,6].

With the advent of Human-Machine Interaction (HMI), this relationship changed to a ’teammate’, in which a human agent has been getting enough information about the surrounding for successful interaction [7,8,9]. As the systems become more advanced (for example, with increasing the levels of automation), the way of information exchange between the human-machine shifted from one level to another depending upon the task given. Therefore, the system needs to cater to sufficient information that properly cultivates the current situation awareness. This is come up with “user-centered” design approach in the ADAS where users’ experience and their expectations are playing a crucial role [10, 11].

This special issue was initiated during the 15th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) from September 18–21, 2023, in Ingolstadt, Germany. For each article, two relevant invited reviewers and the guest editors team evaluated the submitted manuscripts based on its quality. As a result, this special issue presented seven papers on key issues like, in-vehicle air gesture interactions, testing driver warning systems, in-vehicle nudging ACC, truck drivers views on ADAS, prediction of pedestrians crossing behaviours using ML techniques, acceptance of driver-vehicle interaction studies, and perceived security in shared automated vehicles. The articles were dispersed in the following manner: (a) three papers contained a simulator study technique; (b) three papers included online and/or offline surveys; and (c) one paper included a field study that was conducted.

Moustafa et al. (“Sonically-enhanced in-vehicle air gesture interactions: Evaluation of different spearcon comparison rates”) conducted a simulator-based study where twenty-four participants were participated. The study had conducted with four auditory display conditions to measure visual distraction, navigation accuracy, driving performance and workload. Dan Garcia-Carrillo et al. (“Testing driver warning systems for off-load industrial vehicles using a cyber-physical simulator”) also conducted simulation-based study. The authors performed the task using a hybrid testbed using a realistic ADAS and a forklift simulator with thirty-six participants to perform different feedback mechanisms. The warning mechanisms performed based on amber and red LED strips, a LED matrix, and a haptic safety belt method. Claudia et al. (“What is good? Exploring the applicability of a one item measure as a proxy for measuring acceptance in driver-vehicle interaction studies”) performed their task using a BMW driving simulator with sixty-three participants. To validate the applicability of a single-item acceptance measure (SIAM), the authors used Technology Acceptance Model (TAM) and the van der Laan acceptance scale (VDL) in a simulator study.

Martina et al. (“Human or robot? Exploring different avatar appearances to increase perceived security in shared automated vehicles”) conducted scenario-based online questionnaires where participants from Colombia (N = 57), Germany (N = 50), and South Korea (N = 29) were participated. The aim of the study was to measure the anxiety, security, trust, risk, control, threat, and user experience in the shared automated vehicles. Marwa et al. (“Truck drivers’ views on the road safety benefits of advanced driver assistance systems and intelligent transport systems in Tanzania”) conducted a survey (offline) with a two-hundred seven male Tanzanian truck drivers to get an opinion on ADAS. Additionally, their views on the effectively implementing intelligent transportation systems were also gathered. Dungar Singh et al. (“Prediction of pedestrian crossing behaviour at unsignalized intersections using machine learning algorithms: Analysis and comparison”) performed videographic survey with low-to-moderate pedestrian traffic in Bhopal and Mysuru, both cities locate in India. The data included 1183 crossing pedestrian paths with 634 were normal crossing paths and 476 were rolling crossing paths to analysed pedestrian behaviour at the unsignalized interactions.

Finally, Par and Mikael (“In-vehicle nudging for increased Adaptive Cruise Control use: A field study”) performed a field study for ACC on 48 participants in Volvo Cars, Gothenburg to explore whether in-vehicle nudging interventions could be effective or not in the realm of ADAS.

In sum, the empirical findings from seven articles cover vital topics in ADAS especially when we are considering “user” as a center point. We hope that this kind of research will further stimulate more active discussions on various forums and at the same time contribute to the community in the coming time.

Finally, we would like to thank the Editor, Jean-Claude Martin for providing continues and effective support during the preparation of this special issue. We also heartily appreciate volunteer reviewers who not just provided constructive feedback, but also in a timely manner to make the special issue really special.