A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications

Data-Driven User Behavior Evaluation

Automotive Original Equipment Manufacturers (OEMs) compete worldwide to stand out with new trends and technologies. Automated Driver Assistance Systems (ADAS) are an example of advanced solutions where a lot of effort is put into the development and utilization of vehicle data. ADAS systems range from different types of information/warning systems to adaptive functions designed to assist the driver in the driving tasks and ensure more efficient and comfortable driving. These types of systems have become standard at many OEMs, including Tesla, Cadillac, BMW, Mercedes, Volvo Cars, and others. Volvo Cars is well-known for the development of such ADAS functions as ACC (Adaptive Cruise Control) and PA (Pilot Assist). These functions offer lateral and/or longitudinal support, but leave the driver in full control and with responsibility for the driving task.The ADAS systems are not fully automated. These systems have a number of limitations related to the context where they can operate. Previous studies have demonstrated that the drivers’ understanding and adoption of these systems is not definite and may vary from full technology acceptance to complete ignorance. Therefore, in-depth understanding and interpretation of driver behavior and needs regarding the use of ADAS can significantly help developers to reflect on and improve the systems to meet the users’ expectations. Recently, the availability of data coming from the in-vehicle sensors network has increased significantly. The amount of received data potentially enables the in-depth quantitative driver behavior evaluation in a time-efficient and reliable way. Moreover, the ability of vehicle sensors and actuator data to synchronize the driver and system performance and assess the driving conditions in the moment of driver-system interaction can contribute to the comprehensive context-aware ADAS evaluation.\ua0 Developing methods for objective assessment of driver behavior is a task with a high level of complexity. This process requires (i) investigation of the driver behavior assessment area where vehicle data can be useful; (ii) identification of the influencing factors for evaluating ADAS functions; (iii) definition of the relevant data for the data-driven driver behavior evaluation; (iv) investigation of the ways to improve the feasibility of vehicle data. The research presented in this thesis focuses on the understanding of vehicle data applicability in user-related studies. The core of this research is the methodology for objective ADAS evaluation and a mixed-method approach that helps to integrate the quantitative methodologies into existing, mainly qualitative, evaluation practices.The conducted research revealed that vehicle data offers the possibility to determine individual user behavior, and to describe, categorize, and compare this to the average within a group. All of the above mentioned makes the applicability of vehicle data for user-related studies meaningful

Naturalistic driving study for automated driver assistance systems (ADAS) evaluation in the Chinese, Swedish and American markets

In recent years, Automated Driver Assistance Systems (ADAS) have received great promotion and acceptance in the European market. However, transferring ADAS to other markets may affect driver behavior due to the cultural and contextual differences in various markets. Methods used for capturing these differences are based on subjective data collection. This study shows how vehicle data collected in the ND study helps to identify and investigate further the differences in driver behavior and the driving context in the Chinese, Swedish and US markets. This paper discusses a better way to consider the infrastructural and cultural differences in ADAS design

Design of a data-driven communication framework as personalized support for users of ADAS

Recently the automotive industry has made a huge leap forward in Automated Driver Assistance Systems (ADAS) development, increasing the level of driving processes automation. However, ADAS design does not imply any individual support to the driver; this results in a poor understanding of how the ADAS works and its limitations. This type of driver uncertainty regarding ADAS performance can erode the user\u27s trust in the system and result in decreasing situations when the system is in use. This paper presents the design of a data-driven communication framework that can utilize historical and real-time vehicle data to support ADAS users. The data-driven communication framework aims to illustrate the ADAS capabilities and limitations and suggests effective use of the system in real-time driving situations. This type of assistance can improve a driver\u27s understanding of ADAS functionality and encourage its usage

Real-time Personalized Driver Support System for Pilot Assist Promotion in Different Traffic Conditions

The complexity of advanced in-vehicle systems and level of automation provided is currently increasing, making the understanding of smart systems design and limitations challenging to a driver. As a result, misinterpretation of the system\u27s capabilities can be detrimental to perceived usefulness and the system\u27s usage. The personalized real-time driver support concept presented in this paper is designed to improve the driver\u27s understanding of Pilot Assist (PA) and increase PA usage effectiveness in various traffic contexts. The designed communication informs drivers about PA capabilities in various traffic conditions, helping drivers recognize the appropriate context for PA activation and reflect on their own PA use strategy

THE USE of VEHICLE DATA in ADAS DEVELOPMENT, VERIFICATION and FOLLOW-UP on the SYSTEM

Advanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews\u27 analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation

Stepping over the Threshold - Linking Understanding and Usage of Automated Driver Assistance Systems (ADAS)

Automated Driver Assistance Systems (ADAS), which aim to enhance safety and comfort while driving, are becoming increasingly popular in vehicles today. However, ADAS are not yet operative in every situation due to technical limitations, and therefore do not cover all driving situations, traffic, weather and/or road conditions. In order for drivers to use these systems in a safe manner, they need to understand the different modes of operation, as well as the limitations of the systems, or they will not be able to build appropriate trust and adequate usage strategies. Therefore, the purpose of this study was to investigate the factors influencing user understanding of ADAS by implementing an Explanatory Sequential Mixed Methods design. This was done by triangulating data from a Naturalistic Driving (ND) study (132 vehicles) with explanations and reflections from in-depth interviews of purposefully selected participants (12 drivers from the vehicle pool) who were showing different usage patterns. The results show that users’ understanding is influenced by preconceptions about the system, as well as the perceived system performance and usefulness, leading to different levels of trust that affect the users’ engagement with the ADAS. It was found that the driver’s perception of a system does not just change over time, but changes through different situations presented, challenging the expected events and the users’ mental model of the interaction with the system. Therefore, to gain trust and appropriate usage strategies for the ADAS the user needs to overcome potentially negative experiences and challenge the current understanding of the ADAS, by stepping over the threshold

Automotive UX design and data-driven development: Narrowing the gap to support practitioners

The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments). However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving. In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer. We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities. We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs. From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data. We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration. New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process

Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation

Automated Driver Assistance Systems (ADAS) are designed to support the driver and enhance the driving experience. Due to ADAS limitations associated with the driving context, the intended use of ADAS functions is often non-transparent for the end-user. The system performance capabilities affected by the continuously changing driving context influence ADAS usage. However, the cumulative effect of the driving context on driver behavior and ADAS usage is insufficiently covered in the ongoing research. This paper aims to investigate and understand how the driving context affects the use of ADAS. Throughout this research, data from a Naturalistic Driving (ND) study was collected and analyzed. The analysis of the ND data helped to register how drivers use ADAS in different driving conditions and indicated several issues associated with ADAS usage. To be able to clarify the outcomes of quantitative sensor-based data analysis, an explanatory sequential mixed-method design was implemented. The method facilitated the subsequent design of qualitative in-depth interviews with the drivers. The combined data analysis allowed a holistic interpretation and evaluation of the findings regarding the effect of the driving context on ADAS usage. The findings warrant consideration of the driving context as a key factor enabling the effective development of ADAS functions.\ua0\ua9 2020 The Author

Big Data Usage Can Be a Solution for User Behavior Evaluation: An Automotive Industry Example

The current level of User Interface complexity leaves almost no space for subjective assessment of user interaction design. Successful Human-Machine Interface (HMI) assessment can be conveyed through big data analysis of user behavior in the real environment. A series of interviewswith UI/UX engineers from the leading Swedish automotive manufacturer revealed the limitations of existing methods and deficiency of objectiveinformation sources. Design of the case regarding real user data analysis that is presented can bring a better understanding of different users’behavior patterns, which can lead to the improvement of future HMI systems. The data-driven approach can establish a foundation of robustmethodologies regarding the objective evaluation of HMI desig