implemented into the vehicle development process. However, the procedure requires a well labelled test set, as is usual at the application of supervised ML techniques.
To sum up, the methods presented enable a holistic approach of identifying cus-tomer usage profiles with the onboard signals. Complete cuscus-tomer usage profiles can be collected with these methods, once the algorithms have been implemented in mass production vehicles. The main research question of the present research project was the investigation of methods to acquire the customer usage profiles, which has successfully been answered. Once this crowd-sourced data has been collected, it is of major importance to implement a reasonable usage of this data into the vehicle development process. This is discussed by the next Section. Within this industry-sponsored PhD program, the following patent applications have been submitted and are the property of BMW:
• Road curve characteristics PA 2016211739 DE
• Mass estimation PA 2017209746 DE
• Road slope estimation PA 2017209747 DE
• Road roughness classification PA 2017219767 DE
ACT
CREATE
COMMUNICATE AGGREGATE
ANALYSE
Figure 5.1– Information value loop.
to consider these steps not as sequential tasks, but more as a closed loop. The loosely process of arbitrary data generation and later data usage has shown major disadvantages in the past. Therefore, it is convenient to start with the act step of asking the right questions for solving the underlying problem. Once the purpose of the data is identified, the correct acquisition and subsequent analysis of this data can be achieved. The following sub-sections describe the different steps of gathering customer usage profiles.
Act: Defining the purpose
In the present context, the purpose of the data, which is, the customer usage profiles, is improving the vehicle development process by validating the load assumptions. As introduced in Section 1.2, from the viewpoint of durability, the need for customer usage profiles is given. Concerning the vehicle development process, the impact of customer usage profiles covers the definition of initial vehicle specifications, the
derivation of vehicle requirements, the virtual load acquisition and the validation of the vehicle and its components, see Figure1.1.
Create: Data acquisition
First, the customer usage profiles must be acquired on a small scale, which means the methods of gathering customer usage profiles must be implemented into new vehicles. This is the most difficult part, since both the development and the actual implementation of the methods are time- and cost-intensive. Moreover, it is often not possible to calculate a profitable business case for an application that needs CPU capacity, memory and it does not seem to improve the customer experience upon first glance. However, referring to the judgement of Wixom and Ross (see Section 5.5),
“using data to improve operational processes and boost decision-making quality may not be the most glamorous path to monetising data, but it is the most immediate”
[27]. This rethinking from old-fashioned business case-triggered projects to forward-looking data collecting projects is part of the digital transformation and concerning the automotive industry it has only just begun.
Since the implementation of the methods collecting the customer usage profiles is part of a traditional function development for electronic control units (ECUs), it follows the same steps as the vehicle development process. At the beginning, general requirements concerning the customer usage profiles must be defined. This comprises the choice of the features and the classification scheme, which can be multi-dimensional; for example, vehicle velocity versus engine speed. In addition, the resolution of the classification results, which is the bin width, must be defined. It is convenient to collect mutual requirements for the data classification together with other departments and company divisions, since most of the data output is generally also valuable for others. After the requirements have been defined as detailed as possible, the algorithms can be implemented.
It is important to validate the proper function of the data collecting methods to guarantee a high quality of data. Moreover, once the algorithms are implemented into ECUs, it is cost-intensive to improve them by software updates. In addition, data analysis is highly dependent on the data basis. In the past, the most time-consuming part of data analysis was the data preparation and the detection of faulty data sets, including data filtering, outlier detection and plausibility check. In summary, the
detailed definition of data requirements, and the proper implementation including validation and verification, is necessary for an efficient data acquisition.
Communicate: Data transfer
The transmission of the customer usage profiles to the manufacturer can be achieved by automatic read-out procedures during workshop appointments, or by wireless communication via mobile connection. Thus, the data is transferred from the vehicles to a manufacturer’s database.
Aggregate: Data aggregation
Once the raw data of each individual vehicle has been transferred to the database, it must be aggregated and improved to result in a high-quality data set. The ag-gregation from the individual data sets to customer usage profiles is regarded as the transformation from the small to the large scale. This is usually covered by the following steps: data cleaning, data filtering, data joining, calculation of enhanced values, data clustering, interpolating of time stamps and adding of meta data. The result is often referred as a prepared data set which now includes the entirety of observed vehicles.
Analyse: Generating insight
The analysis of the prepared data set generates insight and value. It comprises descriptive statistics, unsupervised ML techniques such as clustering, supervised ML such as classification, pattern recognition, as well as many other statistical methods depending on the purpose. The next section provides some examples regarding statistics.
Act: Improving the vehicle development process
In this research, the value of the customer usage profiles is improving the vehicle development process, ranging from the design stage to the validation procedure.
Each feature of the customer usage profiles addresses a different need, as discussed by the single publications.