Master Thesis project description
Uncertainty-aware road condition segmentation using DL
Background:
Adverse road surface conditions (RSC) like water, snow, and ice increase the risk of accidents on the road. By providing autonomous vehicles with information about such conditions, they can adapt their speed, following distance, or choose alternative routes to reduce these risks. Additionally, unexpected or especially hazardous situations may require human intervention, and autonomous systems should be able to realize this and notify the driver.
Description:
This thesis aims to create an uncertainty-aware DL model that predicts the RSC in front of a moving vehicle while simultaneously estimating its predictive uncertainty, which should ideally be able to detect distribution shifts and out-of-distribution samples.
To achieve this, a dataset is provided that contains two kinds of data: front-facing camera images and point-wise RSC estimates. The latter are measured underneath the vehicle, but they have been fused with the images through motion estimation, resulting in a large, sparsely annotated image segmentation dataset. The dataset composes many different vehicles in different parts of the world which lets us control the degree of distribution shift in holdout datasets.
Methodology
In this project, the student(s) will:
- Research existing methods for uncertainty-aware DL, with a focus on image segmentation.
- Select or design one or more uncertainty-aware DL architectures suitable for the given task and train them on the provided dataset.
- Define suitable holdout datasets and evaluation methods and use them to assess the trained model's ability to estimate its own predictive uncertainty.
Students
The project is intended for 1–2 students with backgrounds in machine learning, and preferably computer vision. Experience with data wrangling, Python, and libraries like PyTorch, is a bonus.
Supervisors
Pontus Andersson and Gustaf Gulliksson, Klimator AB.
Contact
gustaf.gulliksson@klimator.se
Note:
We appreciate your interest in Klimator. Klimator is an equal employment opportunity employer, committed to maintaining a supportive and inclusive workplace for all employees. Klimator does not discriminate against, and prohibits harassment of, any applicant or employee based on race, color, sex, sexual orientation, gender identity, religion, national origin, age or disability.