Smart cars in the context of sensors, actuator technology, and IT

The smart car is developing into an intelligent mobility partner.

1. Objective (why?)
Four megatrends can be cited as drivers:

Fig. 1: Challenges and responses – smart car (source: Dietz Consultants)

To develop a smart car, various capabilities and technologies are integrated to implement the necessary vehicle capabilities. These systems embody complexity. An anticipatory analysis of such systems during system development with FMEA represents a major challenge.

Fig. 2: Necessary capabilities of the smart car (source: Dietz Consultants)

The smart car will tap into and optimize a variety of applications:

  • Active safety and passive safety
  • Car-to-car systems (C2C) – networked vehicles
  • Occupant monitoring
  • E-call
  • The vehicle as living space
  • Diagnostic systems
  • Big data
  • Car-sharing systems
  • Connection of electric vehicles to energy networks
  • Toll systems
  • Billing systems: Consideration of individual vehicle use/driving profiles (e.g. insurance)
  • Security (against attacks)

2. Procedure (how?)
Sensor fusion and control equipment
A variety of sensors are required for all of the stated systems. Their data must be able to replace a variety of human sensory dimensions. The determined sensor data is brought into a context and fused into an overall picture (sensor fusion). The following step is no less complex: Processes for decision-making with the help of fused sensor data and the knowledge from knowledge databases.

Cars nowadays not only have a variety of sensors but also a rapidly growing amount of control equipment. This concept of distributing logic units represents a crucial difference compared to other “smart products”. In contrast to a car, a smartphone only represents a single computer. The near future will show which concept will prevail.

In any case, large quantities of data arise, which are processed (through to personal biometric data such as manual force steering wheel, blinking frequency,..). The systems mentioned above are inevitably concerned with:

  • Saving
  • Processing
  • Communicating

this data. Overall, there is a significant need to legally regulate the scope of this data, which is already collected in vehicles today and is legally in a gray area, for example it is accessed by workshops as part of service work.

FMEA for software-intensive systems
FMEA faces great challenges due to high system complexity. It appears necessary to develop methodical approaches here. The available FMEA tools also require continuous development. We have presented a range of suggestions for applying FMEA for software-intensive products in the UB Dietz Tips & Tricks 10/2014.


  • The various sensory dimensions can be made accessible to the machines by sensors.
  • The determined information must be brought into a context and fused into an overall picture (sensor fusion)
  • This requires a decision-making process for the machine (knowledge database)
  • Sensors in cars work in the background and support the driver. Whether the intelligent triggering of an airbag, a warning of falling sleep or tire pressure measurements

The new Mercedes-Benz E-Class, which will enter series production in the coming years, displays these technologies. The sedans feature two acceleration sensors fitted to the radiator cross member to help classify an impact. Electronics tighten the belt, which pulls the occupants into an optimal sitting position and then releases a fraction of a second later so that the upper body can fall into the airbag. This is triggered in stages depending on the accident situation. In addition, a film under the seat measures the passenger weight. This ensures that the airbag does not hit a petite woman with the same impact as a 220-pound person.

Example smart car: Networked vehicles – car-to-car systems (C2C)
Vehicles communicate directly with each other. For example, the vehicle can pass on information to its immediate surroundings about:

  • An accident situation further down the road
  • Dangerous road conditions such as black ice
  • Available parking spaces

Vehicles that have received information then take on the role of transmitter themselves and pass the data on to following vehicles. The EU has now released the frequencies required for C2C communication in the microwave range.

Fig. 3: Schematic diagram of C2C communication (source: DLR)

Fig. 4: Possible communication content of C2C communication (source: Dietz Consultants)

Smart car example: Occupant monitoring
Monitoring can make an important contribution towards preventing accidents. Recording data is highly challenging from a technical viewpoint and worthy of discussion due to personal data. It is to be assumed that sensing data directly from the driver’s skin will not be accepted (stress and distraction). For instance, the following data can be monitored:

  • Pulse rate changes
  • Change in facial expression
  • Change in blinking frequency
  • Change in the strength with which the steering wheel is held

This and other data then allows the following communication processes to take place:
  • Warning to the driver (such as due to nodding off for a moment)
  • Following an accident: Notification of the responsible emergency response center with the vehicle GPS data

Constant monitoring of the driver can represent a far-reaching intervention into the person’s privacy. The detection of driver activity can collect and interpret far more data than mere tiredness detection. A variety of technical as well as legal developments are required in this field.

3. Result
The “smart rate” for cars will account for a significant proportion of the perceived value of a vehicle in the future. Vehicle manufacturers will shift the focus of their product development onto this. These basic functions of a vehicle are basic requirements and are no longer suitable for competing for customers. Developers receive immense quantities of data on the use of the vehicles and customer behavior. At the same time, cars will change over the course of their service lives. Software updates provide the vehicle with new capabilities. New business models will arise.

The number of network-capable vehicles will grow quickly. According to a study (Oliver Wyman), 80 percent of the cars sold globally in 2016 will already be network-capable. As a result, the quantities of data to be transmitted and process will explode. The car industry thus needs to filter out the information that is relevant to it and that can, for instance, help to create closer customer contact.

This means: Aggregating and making evaluable the various sources of data – including vehicle data, communication data from social media, and customer communication. The various types of big data analysis options will be used here. In short, it can be said that:

  • Vehicles will be a source of data for car drivers, manufacturers, and service providers to an even greater extent
  • Accident-free driving is within reach
  • Vehicles will increasingly become working and living space
  • Driver-vehicle communication will almost take on “human traits”
  • Smart car is an integral part of big data and Industry 4.0

FMEA will remain a leading tool as part of the development-accompanying evaluation of the right system and component design. The FMEA procedural model will be subjected to significant pressure to change.

The elimination of media discontinuities, beginning with the analysis of the customer’s voice, the development of requirements, functional and malfunction analyses, and the derived verification and validation activities, will particularly be given a great deal of attention.

Additional tools, especially FTA and FMEDA, will continue to play a greater role. Significant work must be put into better integrating all of the stated procedures as well as their efficiency.

Fig. 5: Big data smart car (source: Dietz Consultants)