*1.2. Perception*

Machine perception, highlighted in orange in Figure 1, is traditionally vehicle-based. Here, many challenges arise because of the increasingly complex algorithms to process raw sensor data into a reliable digital environment that allows planning of the vehicle trajectory. In addition, environmental conditions such as rain, fog or lighting conditions can deteriorate the machine perception, leading to enhance machine perception with data fused from different sensors.

Due to the high cost for the components and their integration in the vehicle, increasingly sensors located in the roadside environment communicate with the automated vehicle. They shall provide additional information about the static contents such as the road network, roadside infrastructure and buildings, as well as the dynamic content such as moving objects. For the vehicle-to-X communication (V2X) we see different technologies such as dedicated short range communication and mobile communication to allow for data

exchange of a huge amount of data at minimum delay, while maintaining data security and privacy.

#### *1.3. Vehicle Guidance*

Visualized in grey, the driving robot (right side of Figure 1) increasingly performs tasks of the human driver (left side), consisting of mission and trajectory planning and control. The mission planning is something that is an intrinsic human task but widely supported by machine navigation systems. Here, external traffic control systems located in the road infrastructure or cloud-based services can additionally support to optimize transportation tasks by re-routing. However, the most difficult task is the trajectory planning using the horizon offered by the field of view of the human or machine perception. Instead of the traditional approach, namely to deterministically program driving tasks such as for adaptive cruise control, modern methods of artificial intelligence (AI) offer a data driven approach to handle complex and maybe even situations not being experienced before. Nevertheless, the safety validation of AI base trajectory planning is a not solved issue. Vehicle control, usually handled by traditional methods of automation and control, aims to minimize the error in planed and driven trajectories. Here, they need to cooperate with vehicle dynamics control (VDC) systems. Implementing intelligence in the road infrastructure allows for advanced traffic control that maybe even perform trajectory planning as the most delicate step in vehicle control.

#### *1.4. Base Vehicle*

The vehicle, depicted in green in Figure 1, is based on a traditional vehicle but enhanced with actuators, which will evolve from classical steering, power train and braking systems to advanced X-by-wire systems offering new levels of vehicle control.

#### *1.5. Human Machine Interface*

The human–machine interface (HMI) is a delicate component that needs to be designed carefully in order to improve the already high level of reliability in human vehicle control. Literature reports that billions of kilometers need to be driven with an automated vehicle in order to prove statistical significance of a superior behavior of a driving robot. As long as we have the human driver as an operator that needs to perform tasks in vehicle guidance, such as observation of the environment and fallback in case of system failure, the HMI is essential to avoid distraction or inappropriate behavior of the human driver.
