Open Theses

We are constantly looking for new students of local universities to join us to work on our autonomous cars. Background in Robotics (i.e. Robotics class or relevant experience with ROS) is a requirement for the autonomous cars related topics. For possible open thesis topics please contact Daniel Goehring.

Here is a non-complete list of topics which might be of interest for you:

Camera based:

  • Multi-Task Learning using Bayesian Deep Learning uncertainty for Scene Geometry and Semantics (WM-RC).
  • Deep Neural Networks (CNN) for trajectory prediction based on images and odometry data
  • Deep Neural Networks (CNN) for Swarm behavior learning, e.g., learning the desired position of a car within a group of vehicles
  • Fish-eye camera depth estimation
  • Fish-eye camera soiling detection
  • Fish-eye camera traffic sign detection and classification
  • Fish-eye camera weather condition classification
  • Passenger on- and offboarding detection and tracking
  • Road damage detection and classification (RDD2022 Dataset) and 3D localization
  • Road pictogram detection and classification
  • Traffic light recognition using 2d or 3d camera data
  • Turn indicator (blinker) recognition using 2d or 3d camera data
  • Visual slam in traffic situations – using visual features and/or optical flow

LiDAR based:

  • LiDAR construction side beacon detection
  • LiDAR IMU-Coupling (use the accelerometer to avoid LiDAR measurement errors due to vibrations)
  • LiDAR obstruction mapping (would include ground segmentation and 3d
    occluded space modeling)
  • Self-localization and mapping using LiDAR data or point cloud data from stereo cameras
  • Sensor fusion of data from different sensors, e.g., LiDAR with camera data
  • Classification of LiDAR objects using image based classification and the projection of 3d-points into the 2d image
  • Virtual Velodyne scanner from Berlin’s pointcloud data
  • Dynamic obstacle simulation using a virtual LiDAR sensor
  • Semantic Segmentation (VLS 128 / HDL-64)
  • Panoptic Segmentation (VLS 128 / HDL-64)
  • Obstacle detection and tracking (VLS 128 / HDL-64)
  • LiDAR inertial odometry
  • LiDAR wall and curb detection
  • Cross modality localization, e.g. visual localization in LiDAR maps
  • 3D scene reconstruction using LiDAR and/or camera data
  • LiDAR cooperative perception
  • Deep Neural Network-based LiDAR data compression methods for constrained communications

IMU based:

  • Automatic speed bumper detection using smartphones

V2X-Communication:

  • Cooperative driving maneuvers: Forming an emergency corridor
  • Cooperative driving maneuvers: Allow bus to merge into traffic
  • Cooperative moving object tracking

Planning:

  • Calculation of drive splines from road boundary and lane line map data
  • Deep-Learning based trajectory planning, Deep Reinforcement Learning
  • World modeling, planning within a structured dynamic environment
  • Planning of lane changes, merging into traffic using timed elastic bands, or A*, Rapidly Exploring Random Trees
  • Obstacle prediction based on map data

Optimal control:

  • Developing different control strategies to drive on a map and with dynamic objects
  • Analysis of dynamic properties for swarm based control strategies, given certain uncertainties

Simulation:

  • Virtual reality obstacle simulation

Autonomous Modelcar Project Topics:

  • Lanelet2 force field navigation including dynamic obstacles
  • Model predictive control
  • Traffic Light detection and tracking
  • Obstacles detection and tracking
  • Autocharging using inductive charging