How do autonomous cars communicate in everyday traffic?
Within the SALSA project, concepts are being developed to enable comfortable awakening in the vehicle, thereby reducing the risk of “sleep inertia”.
Fraunhofer IOSB
SALSA addresses the challenges that arise when automated vehicles interact with conventional cars and other road users. The project partners have now presented their first results.
SALSA is not about a lively dance, but about the interaction
between conventional and automated vehicles.
The acronym stands for “Smart, Adaptive and Learnable Systems for All” and
refers to a research project funded by the German Federal Ministry for Economic
Affairs and Energy.
The initiative brings together 14 partners from industry and
academia, alongside three associated partners. Since its launch in July 2024,
the project has enabled intensive research activities among participating
organisations. After around 18 months of work, the consortium has now presented
its first results. The overall project duration is three years.
SALSA concept structured into six research areas
The integration of automated and
autonomous vehicles into everyday traffic is becoming increasingly relevant.
Many manufacturers are currently working on both the technical integration and
the broader challenges associated with it.
In essence, SALSA aims to contribute to improved road safety
through further development of automated and autonomous driving technologies.
The project follows a holistic approach that enables knowledge transfer between
different disciplines.
Andrea Elser from Valeo, SALSA project lead, explains: “We
are systematically linking the interior and exterior perspectives. The human
being is always at the centre of our work, both inside and outside the
vehicle.”
To achieve this, the project is structured into several
thematic areas. One focus lies on improving the
user experience of automated driving functions, considering both
passenger cars and commercial vehicles.
Within the research field “Sleep”, project partners are
developing concepts that enable comfortable awakening scenarios in vehicles.
The aim is to reduce the risk of so-called “sleep inertia”, which can occur
when drivers transition from automated driving back to manual control.
SALSA project: key facts
• What is SALSA?
SALSA (“Smart, Adaptive and Learnable Systems for All”) is a German research
project exploring how automated vehicles interact with drivers and other road
users.
• Who is involved?
The consortium includes 14 partners from industry and research, including
Valeo, Audi, Elektrobit, MAN Truck & Bus and the University of Stuttgart.
• What are the research topics?
Key areas include driver state monitoring, external human-machine interfaces
(eHMI), user training concepts and acceptance of
automated vehicles in mixed traffic.
• How is the project funded?
SALSA is funded with €10 million by the German Federal Ministry for Economic
Affairs and Energy and runs for three years.
Another research area focuses on the driver state. Here,
technologies are being developed to comprehensively assess the physical and
mental condition of the driver.
Communication with different road users
A further focus of the project is the development of
external human-machine interfaces (eHMI), which enable new forms of
communication between the vehicle and its surroundings. These concepts take
into account different stakeholders, including cyclists, pedestrians, drivers
of conventional vehicles and potential vehicle buyers.
In the field of communication and adaptation, researchers
are exploring ways to decouple the growing complexity of in-vehicle operating
systems from touchscreen interfaces. Morphing and shape-changing elements could
bring haptic control concepts back into the vehicle interior.
Another thematic area addresses acceptance. Researchers are
analysing how automated and autonomous vehicles are perceived by road users in
mixed traffic environments.
Practical training more effective than manuals
The project also investigates how drivers acquire knowledge
about new vehicle functions and how they interact with automated systems.
According to the researchers, SALSA studies indicate that drivers primarily
learn new functions through experimentation.
Training concepts that combine practical exercises with
interactive and multimedia learning elements appear to be significantly more
effective and motivating than traditional manuals. Particularly relevant for
users are explanations of system operation, limitations, risks and system
indicators. Flexible learning concepts that can be used both before and during
vehicle operation are therefore preferred.
