Workshop Name:
Deep Learning in Automated Driving
Sergiu Nedevschi, TUCN

Location: DoubleTree by Hilton Hotel Cluj – City Plaza, "Beijing" Room (5th floor)


This workshop represents an introduction in deep learning based solutions for driving assistance and automated driving applications. The workshop is based on the authors’ experience gained in several research projects carried out at TUCN dealing with perception tasks. The approached topics are object detection, instance level detection and segmentation and depth enhancement.


  1. Arthur Costea, Deep learning based object detection approaches
  2. Andra Petrovai, Panoptic segmentation for driving environment perception
  3. Vlad-Cristian Miclea, Deep learning techniques for depth perception enhancement



Workshop Name:
Bosch Students Workshop In Automated Driving
Catalin Golban, BOSCH

Location: DoubleTree by Hilton Hotel Cluj – City Plaza, "Beijing" Room (5th floor)

Abstract: Video based driver assistance systems is a continuously growing field in automotive industry. Based on surround sensors such as radar, video and ultrasound, driver assistance systems sense and interpret the traffic scenes. They assist the driver in various driving situations and increase the driving comfort. In addition, driver assistance systems improve driving safety by supporting the driver in critical driving situations that require rapid and safe action. Gradually increasing performance will lead to automated driving solutions available in series cars in the upcoming years.

After an overview of Bosch activities in the video-based driver assistance systems and functions, the focus will be on a set of advanced video development topics .The workshop will present in detail and at tutorial level several advanced stereo video algorithmic methods developed by Bosch R&D engineers in collaboration with students and researchers from Cluj universities in the last years, highlighting the importance of such methods in the context of the growing automated driving industry and showing the beauty and the challenges that appear when putting such methods on embedded hardware running in the car.


  1. Catalin Golban, Overview of automated driving activities in Engineering Center Cluj
  2. Claudiu Mihali, Visual odometry and GPS fusion for improving the localization accuracy
  3. Paul Dragan, 3D data segmentation using deep learning
  4. Paolo Moldovan, Road condition monitoring using deep learning
  5. Csanad Sandor, Optimizing neural networks for embedded systems