Dealing with development risk and complexity in planning situations within product engineering processes

Every product development process is unique and individual. Nevertheless, patterns of recurring and similar elements exist in different processes which experience specific characteristics depending on the type of project. In addition to the different objectives that form the basis of a product development process, projects differ primarily in their share of new development and their degree of complexity. In order to deal appropriately with the resulting uncertainty, implementing agile approaches in processes of mechatronic system development is becoming more popular with the aim of making the development project more flexible. However, it must be borne in mind that not every development process requires an agile approach. Although plan-driven approaches have a poor ability to react to changes, they provide clear structure that leads to a common understanding of the process and a clear definition of objectives. Since a development project does not only contain problems that are well-suited for an agile or a sequential approach it is important to adapt the process to the underlying situation and requirements. In sufficiently plannable situations a purely agile approach would entail the loss of structure. On the other hand, a purely sequential approach for highly uncertain problems means that the process has to be adapted frequently in order to react appropriately to changes and newly acquired knowledge. The approach of ASD – Agile Systems design helps developers to implement suitable development procedures at different process levels depending on the degree of planning stability. In this context, this contribution presents a methodology that examines the influence of new development and complexity on different elements and supports developers in process planning by combining flexible and structuring elements to avoid multiple replanning

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.Comment: Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, and Christin Scheib contributed equally. Accepted for publication at SMC 202

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection