Event Information:

• Wed

  28

  Jan

  2026

  SC Seminar: Abel Philip

  15:00online

  Abel Philip, RPTU University Kaiserslautern-Landau

  Title: A DNN Approach for Multi-Trajectory Prediction for Autonomous Driving
  

  Abstract:

  For safe autonomous driving, accurate trajectory prediction is of utmost importance.
  But factors like changing road geometry and traffic conditions make this a challenging
  task. This means that providing the model with accurate information about the road and
  surroundings needs to be focused on. Most of the approaches researched so far use
  RGB images as the primary input, along with many other inputs formed from processing
  these RGB images. RGB images provide rich spatial information about the scene and the
  other processed inputs are generated in such a way that they reinforce this spatial
  information with further data and improve the performance of the model. This thesis
  proposes a prediction framework that uses depth maps, road masks and vehicle masks
  alongside RGB images to incorporate information about the road structure and the
  surrounding traffic to the model, thereby aiming to provide more accurate road geometry
  and surrounding traffic information. This framework suggests a dedicated
  RoadCurvatureEncoder that uses the road mask to retrieve curvature specific
  information by employing distance transforms, gradient-based operators, and Laplacian
  responses. This information is combined with perception embeddings extracted from
  the RGB images and depth maps, ego motion in the previous time steps and the
  positioning of the surrounding vehicles learned from the vehicle mask. A
  CurvatureAwareRefinementModule then uses the combined information to
  autoregressively generate the future trajectory while trying to maintain the curvature
  extracted from the road mask. This master thesis provides an insight into how the usage
  of the masks has improved the performance of the model on the training dataset and
  tests its cross-town performance in Carla simulation environment.

  How to join online

  You can join online via Zoom, using the following link:
  https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1