FUSION2021 Special Session

Real-time critical perception tasks in the context of automated driving

Organizers
  •  Frank Bieder, PhD Student at Mobile Perception Systems Department, FZI
  •  Martin Lauer, Group Leader at Institute of Measurement and Control Systems, KIT
  •  Wei Tian, Assistant Professor at School of Automotive Studies, Tongji University
  •  Thao Dang, Professor at Signal Processing Lab, Esslingen University of Applied Science

Topic
In recent years, huge progress has been made in the development of algorithmic solutions for automated vehicles. In this context, perceiving and modeling the current state of the ego-vehicle as well as the surrounding traffic scene is one of the core elements. In safety-critical applications such as automated driving it is crucial to incorporate heterogeneous data from multiple sensors to obtain redundancy and maximize the amount of information. With the recent developments in remote sensing and processing technologies, the variety and quantity of sensor data is rapidly increasing. This demands for equal advancements in information fusion systems making them a key component for the successful deployment of automated vehicles. The fact that automated vehicles are being operated in highly dynamic environments additionally adds severe constraints on the execution time in order to achieve humanlike reaction times.

In this special session, we welcome submissions on recent advances in solving real-time critical perception tasks in the context of automated driving. These tasks may include various steps towards a holistic scene understanding in urban scenarios, including environmental modeling, multi-sensor fusion systems, extended object detection and tracking and semantic scene classification. We want to discuss novel data fusion strategies and bring together researchers from academia and industry to push forward perception for automated driving. A focus on the fusion of heterogeneous sensor data is encouraged, yet not required.

  https://www.fusion2021.co.za/call-for-papers/

FUSION2021 Special Session