We are constantly looking for new students of local universities to join us to work on our autonomous cars. 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 distortion rectification with GANs: Training on Fish-Eye front with normal camera as Ground-Truth
- Fish-eye camera soiling detection
- Fish-eye camera traffic sign detection and classification
- Fish-eye camera weather condition classification
- Road damage detection and classification
- 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
- Semantic Segmentation (VLS 128 / HDL-64)
- Panoptic Segmentation (VLS 128 / HDL-64)
- Obstacle detection and tracking (VLS 128 / HDL-64)
- LiDAR inertial odometry
- Cross modality localization, e.g. visual localization in LiDAR maps
- 3D scene reconstruction using laser and/or visual sensors
Planning:
- Calculation of drive splines from road boundary and lane line map data
- 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
- Ananlysis 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