Teaching

Context: Autonomous vehicles, like other robots, need to localize themselves in order to navigate. While satellite navigation systems (GNSS) such as GPS can provide such vehicles with localization information, the GNSS information might not always be available. One robust technique for vehicles to localize is using particle filters (e.g. [1], [2], [3]), given a map of the environment.

The project: During the project, you will prototype the use of milestone-board information for map-based localization. The investigation includes the following steps:
You will begin by first creating a scenario (i.e. as a Python or Matlab program, for example) that contains some highways, roads, cities, and virtual milestone boards.
You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization within the map (i.e. inside the created scenario). This also includes exploring multiple possible routes that lead to the same city.

Context: Autonomous vehicles, like other robots, need to localize themselves in order to navigate. While satellite navigation systems (GNSS) such as GPS can provide such vehicles with localization information, the GNSS information might not always be available. One robust technique for vehicles to localize is using particle filters (e.g. [1], [2], [3]), given a map of the environment.

The project: During the project, you will investigate the use of distances to cities, essentially milestone board information, to implement map-based-localization in Open Street Map for Estonia. The investigation includes the following steps:
(i) You will begin by familiarizing yourself with OpenStreetMap and creating virtual milestone boards on a set of testing routes.
(ii) You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization with the map, using the information from the milestone boards as your measurements.

Context: 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.

Description: You will build upon the innovative work done on flow-sensing applications in autonomous driving by Roman Matvejev [1] and Matis Ottan [2]. It is possible to investigate one of the following avenues: (i) working in simulations and proposing new applications of flow sensing in autonomous driving, (ii) investigating new feature extraction and classification/regression methods etc., (iii) expanding out of simulations with physical validation of flow sensing in autonomous driving.

Autonomous vehicles, like other robots, need to localize themselves in order to navigate. While satellite navigation systems (GNSS) such as GPS can provide such vehicles with localization information, the GNSS information might not always be available. One robust technique for vehicles to localize is using particle filters (e.g. [1], [2], [3]), given a map of the environment.

During the project, you will investigate the use of vision sensing for map-based localization. The investigation includes the following steps:
1. Recognition of street names (and possibly also road direction signs) using vision
2. Matching of the perceived street-name information with streets in a given map (such as the Open Street Map)
3. Implementing a particle filter for localization within the map using the above-mentioned matches
4. Incorporation of the developed solution into the Autoware Mini software stack — https://adl.cs.ut.ee/lab/software

Context: Autonomous vehicles, like other robots, need to localize themselves in order to navigate. While satellite navigation systems (GNSS) such as GPS can provide such vehicles with localization information, the GNSS information might not always be available. One robust technique for vehicles to localize is using particle filter [1, 2, 3], given a map of the environment. Recently, deep-learning approaches to map-based localization have also been proposed in the literature [4, 5].

Project: During the project, you will investigate lidar sensors for map-based localization. You can start by experimenting with lidar data acquired using the ADL Lexus vehicle and then move to real-time implementation on-board the vehicle (within our Autoware Mini software stack - https://adl.cs.ut.ee/lab/software). The investigation includes aspects such as:

(i) Creating a map, using data from the Estonian Land Board, and implementation of a map-based localization using particle filtering. The validation can be done in two ways:
- In the CARLA simulation of Tartu city.
- Using real lidar data from the streets of Tartu (we already have this data at the lab).

(ii) Using the above implementation, investigate aspects such as: what is the lower bound on how rich the lidar sensor data should be, in order to perform localization? The vehicle is equipped with multi-beam lidars from Velodyne and Ouster. So the natural question is if we need all the laser beams? Do we need 16, 8, or is even a single beam enough?

(iii) Incorporation of your proposed solution into Autoware Mini.

The testing of Automated Driving Systems (ADS), like our Institute's Bolt car, is difficult and costly. Using simulators is an alternative but has the challenge that it is not easy to transfer test results from the simulated ADS (the so-called ego car) to the real-world ADS. The exact details of the thesis topic depend on the previous knowledge and interests of the student.