Daimler has worked with numerous renowned research institutions in the field of artificial intelligence for years. Daimler has been expanding its network to universities with innovative instruments such as “Forschungscampus”, tech centers, shared professorships and industry fellowships and start-up incubators and accelerators. These efforts, and the new alliance with CSAIL, reflect the importance of artificial intelligence for Mercedes-Benz.

Artificial intelligence is a key future topic for Mercedes-Benz, in-car and beyond, such as in the fields of mobility services or in development and production. Artificial intelligence has ceased to be science fiction and the progress in autonomous driving is an impressive proof of this. Likewise, AI already assists the development phase and production by providing intuitive access to global knowledge and knowhow—always tailored to the individual needs, experiences and knowledge of the employee.

Mercedes-Benz is rigorously advancing its research and development in different directions so as to continue to play a pioneering role in the automotive industry in the development and application of artificial intelligence. The new cooperation with the MIT ideally complements this. The partnership enables us to benefit even more directly from the research results of a leading world institute and to network with the best brains. We aim to continue to play a leading role in shaping the future of mobility—with new mobility concepts, with cognitive cars and services which focus on people and make their daily lives easier and better.

—Anke Kleinschmit, Head of Daimler Group Research

An important objective of Mercedes-Benz’ activities relating to artificial intelligence is the development of cognitive vehicles. Such vehicles are not only able to respond to certain situations; they have enough knowledge about their environment to be able to act autonomously on this basis. Coupled with corresponding services they could become the fundament for a holistic mobility eco-system of the future, Daimler suggests.

For example, cognitive vehicles could autonomously analyze the current traffic situation for all forms of transport and draw up an individual mobility plan that suits the customer’s personal daily routine and mood. In addition, household robots and delivery drones could be linked to the system with the cognitive car as the control centre for this.

Unlike smartphones and wearables, the car would surround the person and become a surrounding for a digital experience. It could analyze the driver’s behavior, interpret needs and adapt accordingly. It would be able to identify what he or she wants in certain situations and what he or she needs. Examples of this are playing the right music to suit the current mood, setting the most pleasant temperature or developing services relating to health and safety. Moreover, the cognitive vehicle would offer self-determined access to an individualized artificial intelligence which supports human beings, entertains them and could even challenge them intellectually.

(At CES 2017, Honda will showcase the NeuV, a concept automated EV commuter vehicle equipped with artificial intelligence (AI) called the “emotion engine” that creates new possibilities for human interaction and new value for customers. The “emotion engine” is a set of AI technologies developed by cocoro SB Corp., which enable machines artificially to generate their own emotions. (Earlier post.)

Image and pattern recognition as an important milestone on the way to autonomous driving. To successfully embark on this path, vehicles must be able to acquire knowledge about their environment as well as analyse it. This machine learning already plays an important role for autonomous driving as of today.

Mercedes-Benz is working intensively on the further optimization of automatic image and pattern recognition for driver assistance systems and autonomously driving vehicles. A decisive topic here is the interaction of cameras, sensors and the associated computing units—i.e., sensor fusion.

The system breaks down the pictures of road scenes into abstract segments with colored marking. In this way it identifies buildings, vehicles, persons, trees and pavements among other things and reliably finds traffic lights as well as smaller dangerous obstructions on the road. Based on this, the autonomous vehicle analyses the traffic situation, predicts the behaviour of other road users and decides on its own behavior.

In daylight many systems for image and pattern recognition, on the market are reliable. Meanwhile, our system even offers top level results at night and that is a major development. The next step is about recognizing and interpreting people’s gestures and facial expressions.

—Dr Uwe Franke, responsible for image recognition/signal processing and sensor fusion in the Mercedes-Benz development department

The recognition of gestures, facial expressions and people’s understanding of machine behavior makes an operative interaction between man and autonomous vehicles possible. On this basis, trust can be created between humans and machines. Vehicles must be able to make it clear that they recognize pedestrians and pay attention to them. Pedestrians must receive information about where an autonomous vehicle is going, how it will behave in the next few moments and how they should behave themselves.

Finding and making faster use of ideas and potential with AI. Mercedes-Benz is not just using artificial intelligence with regard to its vehicles. Among other things the company is testing self-learning systems in the observation of technology trends, in the interpretation of development and test data as well as for the industrial maintenance of its production and manufacturing facilities.

Artificial intelligence can make a decisive contribution to diagnosing technical problems. For example, until now production maintenance staff either had to search through huge amounts of documents or fall back on their personal experience to get information about machine defects. The tested system handles documentation with natural language processing and serves as a semantic search engine. Unlike a keyword-based search engine the focus is on the meaning of the request. This enables requests in the form of various fault descriptions, for example “oil is leaking” or “leaking pipes”. In this way repair and maintenance processes can be speeded up and made more efficient.