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 12.1.5: 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.

Thesis chapter 11: Architecture for Large-Scale Innovation Experiment Systems

This chapter explores architectures when innovation experiment systems is used as a development approach to embedded software, i.e. when an organisation operates at approach A from chapter 7.
A shorter version of this chapter is previously published as

U. Eklund and J. Bosch. “Architecture for Large-Scale Innovation Experiment Systems”. Proceedings of the WICSA/ECSA. Helsinki, Finland: IEEE Computer Society, 2012, pp. 244–248. isbn: 978-0-7695-4827-2. doi: 10.1109/WICSA-ECSA.212.38.

Abstract

Business and design decisions regarding software development should be based on data, not opinions among developers, domain experts or managers. The company running the most and fastest experiments among the customer base against the lowest cost per experiment outcompetes others by having the data to engineer products with outstanding qualities such as power consumption and user experience.
Innovation experiment systems for mass-produced devices with embedded software is an evolution of current R&D practices, going from where innovations are internally evaluated by the original equipment manufacturer to where they are tried by real users in a scale relevant to the full customer base. The turnaround time from developing and deploying an embedded product to getting customer feedback is decreased to weeks, the limit being the speed of the software development teams.
The paper presents an embedded architecture for realising such a novel innovation experiment system based on a set of scenarios of what to evaluate in the experiments. A case is presented implementing an architecture in a prototype in-vehicle infotainment system where

Thesis chapter 2.4: Innovation

Innovative ideas for embedded products are typically collected and prioritized during the roadmapping and requirement management process as part of the yearly release cycle. This cycle is usually determined by manufacturing concerns of the hardware. Feedbacks on innovations from real customers are collected only on new product models, if collected at all.

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 (link to Ries, link to Bosch}, 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.

The innovation experiment system focuses on incremental innovation according to the framework by Henderson and Clark. The short iterations of experiments are primarily aimed to refine and extend established designs. Improvement occurs in individual software parts, but the underlying design concept remain mostly unchanged, even if there is nothing that prohibits the evaluation of e.g. architectural innovations as well.