The EAI IC4S international conference has awarded the prize for the best paper to Fraunhofer IKS. The topic of the paper is a concept for safe and efficient collaboration between autonomous mobile robots (AMR) and people in shared areas.

For autonomous vehicles to move smoothly and safely through road traffic in the future, they must be able to communicate with other road users and with the traffic infrastructure. To support the communication, Fraunhofer IKS has developed a new approach for such hybrid perception.

Deep Neural Networks (DNNs) have proven their potential. This applies above all to their use in the laboratory. Their performance in real-world applications leaves a lot to be desired. One major problem is the estimation of uncertainty. A closer look at methods that meet this challenge.

When a system malfunctions or a required service is not provisioned in a timely manner, this can lead to human injuries or fatalities. These situations are considered safety-critical, e.g. in automated driving and human robot interaction. For this reason, the systems that are involved must exhibit a high degree of dependability. To determine a system’s dependability, scientists at Fraunhofer IKS are developing an adaptive, dynamic and flexible safety analysis process that overcomes the limitations of current methods.