Future car trends?

Here are some external links that might be of interest. I cannot vouch for the accuracy of the trend predictions (lets say they don't fulfill the criteria for scientific papers), but they give input to forming personal opinions.

• A comprehensive study on innovation in the automotive industry. Note that the report is from 2007 and much of what they predict to 2015 is still far away...
• Think How Much Smarter Your Car Will Be In A Few Years. Some insight into cutting edge car technology.

Thesis chapter 12.3: Future work

Suggestions for future work are:
A successful transition to more autonomous development on a team level seem to depend on dedication and enthusiasm among developers, strong domain knowledge in the team(s), stable interfaces to other systems, and a good systems engineering foundation (in the form of a systems design or architecture). Further studies of sufficient prerequisites should be of great interest to practitioners and researchers alike.
Further research on how to mitigate issues with synchronisation and integration of many teams in large projects, where scaling of agile practices is just a special case. Of special interest is if the responsibility can be completely moved from the process to the architecture, creating a completely composable system where successful integration is assured just by following the architecture.
More studies with industrial validation of innovation experiment systems for embedded systems are needed.

Thesis chapter 12.2: Summary of contributions

The thesis provides the following contributions:

First, it presents a rich insight in the industrial development of embedded software through a number of industrial cases. The deep description is valuable both for researchers to better understand the relevance of potential research problems, and for professional practitioners to relate the context they are working in with other organisations.

Second, it presents a model of 5 approaches of industrial development of embedded software, ranging from integration-centric development focusing on. This model describes the approaches in more than one dimension, highlighting that differences between R&D approaches is not seen in e.g. just the process dimension, which is new.

Third, it defines a model for the interaction between individual development teams and the organisation as a whole, and based on this model a set of prescriptive measures supporting individual teams adopting agile development methods.

Fourth, it defines a novel reference architecture for composition of independently developed embedded software applications, suitable for using in an open software ecosystem. Open software ecosystems are not new, but no reference for implementation is published in literature.

Fifth, it defines an architecture for innovation experiment systems for embedded software. The concept of innovation experiment systems in this domain is completely new and the architecture is the first of its kind.

The artefacts developed above are all tried and evaluated in an industrial context, i.e. in a “real” project setting with professional practitioners.

Thesis chapter 12.1.7: Is automotive software different?

The context for the automotive industry regarding software development is not different compared to other embedded domains. This conclusion is based on the mapping study in chapter 7 over different approaches of embedded software development and the case studies performed in chapters 5 and 6.

Thesis chapter 12.1.6: Design goals

The leadtime reduction is supported by maximising the speed of the individual teams through their way-of-working, and by decouple the teams form each other through the composability of the architecture.

The ability to frequently deliver new software features is achieved both on a team level for the same reasons supporting the leadtime reduction form idea to implementation, but also through the concept of innovation experiment systems.

The decoupling of software from hardware development is achieved both by moving from centralised synchronised processes to more autonomous teams, and by providing suitable abstractions in the embedded platform underlying the application/feature software.

Thesis chapter 12.1.5: Research answer 1.4

Since more and more embedded products also are connected, it is conceivable to develop, deploy and measure usage on new software in iterations which lengths are determined by the speed of the software development teams instead of the setup of the manufacturing process, going from years to weeks. Such
an innovation experiment system (IES) would utilise feedback from real users of the embedded products in a scale comparable to the entire customer base.
The notion of continuous innovation is not new, but the concept is novel in the embedded domain.
The driver for having such an IES is that business and design decisions should be based on data, not opinions among developers, domain experts or managers. The company running the most experiments among the customer base against the lowest cost per experiment outcompetes the others by having the decision basis to engineer products with outstanding customer experience.
Chapter 11 presents three architectures to support IES for mass-produced devices with embedded software, which together with an infrastructure capable of collecting and analysing the data. Case VI implemented the thin client architecture for innovation experiments from chapter 11 and ran an experiment
collecting data from 7 users.
The conclusion is that it is technically feasible to implement an IES with the architecture defined in chapter 11, and that the measured data can support conclusions about implemented designs. The main contribution is the architecture for innovation experiment systems for embedded software. The concept of innovation experiment systems in this domain is completely new and the architecture is the first of its kind.