So you’re thinking of making the culmination of your studies about self-driving?
Good, because we need all the help we can get. See all the available thesis topics and don’t hesitate to contact us if your idea for a self-driving topic is not listed!
Flow sensing for applications in autonomous driving
The usual sensing modalities that are available to autonomous vehicles i.e. vision, radar and lidar have their limitations. For example, none of them is very suitable for estimating the length of a leading vehicle. Additional sensing modalities, such as wind flow sensing, might have the potential to meaningfully complement the usual sensing modalities in autonomous driving.You will build upon the innovative work done on flow-sensing applications in autonomous driving two previous theses at ADL There are multiple directions that can be investigated. For example, (i) working in simulations and proposing new applications of flow sensing in autonomous driving, (ii) investigating new feature extraction and classification/regression methods etc. or (iii) expanding out of simulations with physical validation of flow sensing in autonomous driving.
Map-based localization in autonomous driving using lidar
Crossview geolocalization in non-urban environments for autonomous driving
Create materials to teach data science via self-driving
Developing a course (8 academic hours one-day seminar or a 3 ECTS course) teaching key aspects of data science in a practical way with self-driving toy cars. The particular emphasis is on aspects that become relevant when deploying a solution to the real world. The toy cars are equipped with a camera and a Raspberry Pi. The used machine learning solution imitates human driving, learning the function between the camera image and the command a human would give when seeing this image. As the task is fun and understandable, the failures are evident (car crashes or behaves weirdly), it is a very good medium to teach data science and machine learning. The course should cover key concepts that get mentioned in every data science, but rarely practically experienced by students, such as the following. (i) Garbage in, garbage out. If your training data is low quality, you get a bad model. Can be exemplified by using too low camera resolution for the task (garbage input) or bad driving examples (garbage training labels). (ii) Overfitting and finding non-causal relationships in the data, which later lead to bad generalization ability. Exemplified by visualizing the image areas that influence the decision the strongest (saliency maps). If top half of the image is not cut away, the model will learn to rely on lamp locations in the ceiling, not track walls for deciding when to turn. (iii) Models struggle to extrapolate, generalize. Exemplified by models failing to drive if light conditions change. (iv) Test set metrics are not the final product, the model will be deployed in the real world and will find examples it can not deal with. Or will experience a shift in input distribution. Monitoring performance, characterizing failures and reacting to them is important. (v) Dataset management, iteratively building a better dataset (to fix the failures observed), and data cleaning are important. Exemplified by results before and after. (vi) Computational efficiency can be important in certain tasks. If the vehicle takes too few decisions per second it will crash. In other domains - if every query costs too much (on compute bill) or takes too long to compute, the product is not viable. The thesis should make use of known theories of developing educational materials, e.g. define learning objectives (what should the student know) and work backwards from there. In particular for a MSc thesis, the educational theory background is needed for a good mark. MSc thesis would also benefit from running the developed course once with test students. BSc thesis can get away without actual experimentation due to time constraints.