Driving automation: Learning from aviation about design philosophies

Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation

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

The Modularisation Design Approach Applied to the ADAS Domain: The DESERVE Project Experience

The paper focuses on the innovative strength that the DESERVE platform has brought on the Advanced Driver Assistance Systems (ADAS) market in terms of major safety and economic affordability. DESERVE is a project aimed at designing and implementing a low-cost, integrated platform for ADAS: the creation of innovative software and hardware modules to be integrated in ADAS applications will pave the way to a standardization of the single components in order to achieve a full integration of diversified models despite their complexity. The achievement of such objective will end up in an increase of the reliability level of the system and in a cost reduction for ADAS functions and for development costs as well. In this paper the results of the application of the modularisation philosophy to the DESERVE platform architecture and to the human machine interface (HMI) concepts will be presented

Intelligent driver profiling system for cars – a basic concept

Many industries have been transformed by the provision of service solutions characterised by personalisation and customisation - most dramatically the development of the iPhone. Personalisation and customisation stand to make an impact on cars and mobility in comparable ways. The automobile industry has a major role to play in this change, with moves towards electric vehicles, auton-omous cars, and car sharing as a service. These developments are likely to bring disruptive changes to the business of car manufacturers as well as to drivers. However, in the automobile industry, both the user's preferences and demands and also safety issues need to be confronted since the frequent use of different makes and models of cars, implied by car sharing, entails several risks due to variations in car controls depending on the manufacturer. Two constituencies, in particular, are likely to experience even more difficulties than they already do at present, namely older people and those with capability variations. To overcome these challenges, and as a means to empower a wide car user base, the paper here presents a basic concept of an intelligent driver profiling system for cars: the sys-tem would enable various car characteristics to be tailored according to individual driver-dependent profiles. It is intended that wherever possible the system will personalise the characteristics of individual car components; where this is not possible, however, an initial customisation will be performed

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

ADAS at Work: assessing professional bus drivers\u27 experience and acceptance of a narrow navigation system.

Due to the argued benefits of passenger comfort, cost savings, and road safety, the bus sector is showing increasing interest in advanced driver-assistance systems (ADAS). Despite this growth of interest in ADAS and the fact that work tasks are sometimes complicated (especially docking at bus-stops which may occur several hundred times per shift), there has been little research into ADAS in buses. Therefore, the aim of this study was to develop further knowledge of how professional bus drivers experience and accept an ADAS which can help them dock at bus-stops. The study was conducted on a public route in an industrial area with five different bus-stops. Ten professional bus drivers got to use a narrow navigation system (NNS) that could dock automatically at bus-stops. The participants’ experience and acceptance were investigated using objective as well as subjective data (during and after the test-drive) and data were collected using interviews, questionnaires, and video recordings. The participants indicated high levels of trust in and acceptance of the NNS and felt that it had multiple benefits in terms of cognitive and physical ergonomics, safety, and comfort. However, the relatively slow docking process (which was deemed comfortable) was also expected to negatively affect, e.g., timetabling, possibly resulting in high stress levels. Therefore, when investigating users’ acceptance of ADAS in a work context, it is important to consider acceptance in terms of the operation, use, and work system levels and how those levels interact and affect each other