A first approach to understanding and measuring naturalness in driver-car interaction

With technology changing the nature of the driving task, qualitative methods can help designers understand and measure driver-car interaction naturalness. Fifteen drivers were interviewed at length in their own parked cars using ethnographically-inspired questions probing issues of interaction salience, expectation, feelings, desires and meanings. Thematic analysis and content analysis found five distinct components relating to 'rich physical' aspects of natural feeling interaction typified by richer physical, analogue, tactile styles of interaction and control. Further components relate to humanlike, intelligent, assistive, socially-aware 'perceived behaviours' of the car. The advantages and challenges of a naturalness-based approach are discussed and ten cognitive component constructs of driver-car naturalness are proposed. These may eventually be applied as a checklist in automotive interaction design.This research was fully funded by a research grant from Jaguar Land Rover, and partially funded by project n.220050/F11 granted by Research Council of Norway

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

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

Intravehicular, Short- and Long-Range Communication Information Fusion for Providing Safe Speed Warnings

Inappropriate speed is a relevant concurrent factor in many traffic accidents. Moreover, in recent years, traffic accidents numbers in Spain have fallen sharply, but this reduction has not been so significant on single carriageway roads. These infrastructures have less equipment than high-capacity roads, therefore measures to reduce accidents on them should be implemented in vehicles. This article describes the development and analysis of the impact on the driver of a warning system for the safe speed on each road section in terms of geometry, the presence of traffic jams, weather conditions, type of vehicle and actual driving conditions. This system is based on an application for smartphones and includes knowledge of the vehicle position via Ground Positioning System (GPS), access to intravehicular information from onboard sensors through the Controller Area Network (CAN) bus, vehicle data entry by the driver, access to roadside information (short-range communications) and access to a centralized server with information about the road in the current and following sections of the route (long-range communications). Using this information, the system calculates the safe speed, recommends the appropriate speed in advance in the following sections and provides warnings to the driver. Finally, data are sent from vehicles to a server to generate new information to disseminate to other users or to supervise drivers’ behaviour. Tests in a driving simulator have been used to define the system warnings and Human Machine Interface (HMI) and final tests have been performed on real roads in order to analyze the effect of the system on driver behavior

HeteroGenius: A Framework for Hybrid Analysis of Heterogeneous Software Specifications

Nowadays, software artifacts are ubiquitous in our lives being an essential part of home appliances, cars, cell phones, and even in more critical activities like aeronautics and health sciences. In this context software failures may produce enormous losses, either economical or, in the worst case, in human lives. Software analysis is an area in software engineering concerned with the application of diverse techniques in order to prove the absence of errors in software pieces. In many cases different analysis techniques are applied by following specific methodological combinations that ensure better results. These interactions between tools are usually carried out at the user level and it is not supported by the tools. In this work we present HeteroGenius, a framework conceived to develop tools that allow users to perform hybrid analysis of heterogeneous software specifications. HeteroGenius was designed prioritising the possibility of adding new specification languages and analysis tools and enabling a synergic relation of the techniques under a graphical interface satisfying several well-known usability enhancement criteria. As a case-study we implemented the functionality of Dynamite on top of HeteroGenius.Comment: In Proceedings LAFM 2013, arXiv:1401.056

Assessing the value of the information provision for enhancing the autonomy of mobility impaired users. Madrid pilot Site Study.

A City is the space where every person acquires the citizen condition, which demands access to multiple services and facilities, and develops social relations in a free and equal condition of options. A lack of accessibility limits independency and autonomy. Thus, the relationship between “sustainable development” and “accessibility for all” becomes clearer, and both goals reinforce each other. In this sense, information plays a key role in order to overcome existing barriers, specially for people who rarely use public transport, have impaired mobility, or make a particular journey for the first time. The impact and benefits is linked with public transport as a “facilitator” of mobility, and, in particular, for the aim of intermodality. The usefulness of information that should be provided (both the information itself and how is offered) to mobility impaired users (MI users) is discussed on this paper based on following of the ASK-IT project that has being carry out on Madrid. The work was done in close cooperation with representatives of all different types of MI user groups

An MCDM approach to the selection of novel technologies for innovative in-vehicle information systems

Driving a car is a complex skill that includes interacting with multiple systems inside the vehicle. Today’s challenge in the automotive industry is to produce innovative In-Vehicle Information Systems (IVIS) that are pleasant to use and satisfy the costumers’ needs while, simultaneously, maintaining the delicate balance of primary task vs. secondary tasks while driving. The authors report a MCDM approach for rank ordering a large heterogeneous set of human-machine interaction technologies; the final set consisted of hundred and one candidates. They measured candidate technologies on eight qualitative criteria that were defined by domain experts, using a group decision-making approach. The main objective was ordering alternatives by their decision score, not the selection of one or a small set of them. The authors’ approach assisted decision makers in exploring the characteristics of the most promising technologies and they focused on analyzing the technologies in the top quartile, as measured by their MCDM model. Further, a clustering analysis of the top quartile revealed the presence of important criteria trade-offs.Operational Competitiveness Program – COMPETE, QREN (Quadro de Referência Estratégico Nacional), European Regional Development Funds (European Union), R&D project in joint-promotion (HMIEXCEL-2013-2015 36265/2013) HMIEXCEL - I&D crítica em torno do ciclo de desenvolvimento e produção de soluções multimédia avançadas para automóvelStrategic program FCT-UID/EEA/00066/2013Fundação para a Ciência e a Tecnologia (IF/00217/2013)Fundação para a Ciência e a Tecnologia (PD/BD/105966/2014

Draft guidelines concerning E&D issues: The TELSCAN handbook of design guidelines for usability of systems by elderly and disabled drivers and travellers. Version 2

Draft guidelines concerning E&D issues: The TELSCAN handbook of design guidelines for usability of systems by elderly and disabled drivers and travellers. Version