The best way to start your career is to tackle an existing problem with the support of some of the best and brightest minds in the field. Our summer internship is a great opportunity to get valuable, first-hand experience by working on interesting projects related to computer vision and machine learning.

This page provides an overview of past internship topics that have been successfully tackled by our interns. To stay up to date with the upcoming internship opportunities, follow us on Facebook and LinkedIn.


Automotive Division Internship 2021

Sound classification using CNN

Convolutional neural networks proved to be very effective for image processing, and are increasingly being used for sound processing. Indeed, by computing spectrograms of audio signals, we obtain visual representations of their spectrum of frequencies. These images can then be readily used in CNNs. The goal of the project is to make a brief review of the literature, implement a CNN for sound classification for an application of choice, and experiment with different hyperparameters, particularly those specific to audio data.

GAN for generating adverse weather conditions

Adverse weather conditions such as rain or snow can have a significant impact on autonomous driving performance. At the same time, such data can often be difficult to obtain. Therefore, advanced data augmentation techniques can be used to expand existing datasets. One of the newer approaches is to use a generative adversarial network (GAN) to generate synthetic data. The goal of this project is to develop a GAN for generating adverse weather condition scenarios.

Automatic Emergency Braking Environment

Automatic emergency braking (AEB) is a staple of modern advanced driver-assistance systems. Its purpose is to mitigate crashes by initiating braking automatically when hazardous conditions arise. The purpose of this project is to explore the existing AEB algorithms in the available body of scientific work and to develop a custom algorithm and testing environment evaluating the potential for AEB activation on a given dataset and its resimulations given different changes in the object detection and tracking pipeline. The solution is expected to blend seamlessly with the existing environments using the existing resimulation pipelines and build systems.


Automotive Division Internship 2020

Generate artificial data using generative adversarial network

Data collection and data marking are usually done manually and can prove to be a tedious, expansive and error-prone process. In order to increase the amount of data and improve robustness of a dataset, artificial data approaches can be used. The goal of this internship project is to use generative adversarial network (GAN) to generate usable artificial data. The student will learn how to implement GAN, train GAN on in-house dataset and have an opportunity to further investigate different models and approaches to improve results. Prior experience in the field of machine learning is preferable, but not mandatory. The estimated duration of this project is six to eight weeks.

Data Reduction: Removing Uninformative Samples

Consider developing a supervised learning method with data stemming from a video, for instance a forward-looking camera for an autonomous vehicle. It is guaranteed that many training samples coming from consecutive frames of a video will be too similar. Such (almost) repetitive samples may lead to overfitting, while at the same time significantly increasing training time. The goal of this internship project is to develop a preprocessing step to detect and remove such samples. The task can and will be tackled in different ways, including “conventional” methods and ML algorithms, and gradually progressing from simpler to more complex formulations of the problem.

Object tracking with UKF (for constrained nonlinear systems).

This topic is in the area of object tracking applied in automotive industry. During the internship period, the student will get an insight into modelling a complex system behaviour such as behaviour of a vehicle as well as how to utilize Kalman filter for object tracking. Furthermore, the student will gain practical knowledge with implementation of Extended Kalman Filter, Unscented Kalman filter, and, time allowing, introduction of state constraints in Kalman filtering. Knowledge of estimation theory as well as basic understanding of linear algebra are needed to successfully finish this project. State constraints are an interesting topic and successfully implemented in model predictive control, but there are also articles that suggest that they can be used with EKF and UKF as well. During this internship, it is encouraged to investigate approaches how to do it, with an input from mentor, and try to implement it in UKF. If the work proves engaging and estimation of work inadequate, the internship can be extended for two more weeks. However, if for any reason the implementation cannot be done, just getting an insight in state constraints with Kalman filters would be considered beneficial.

Using a game engine to create synthetic training data

Obtaining real world data needed to train a deep neural network is usually a challenging, costly and time consuming task. The most labor intensive part is manually marking a large amount of data for supervised learning. One way to overcome this issue is to create synthetic data that can be automatically marked using a game engine. The goal of this project will be to create a rather innovative synthetic “domain randomized” dataset similar to the one described in this article that can be used to train a traffic light recognition CNN.

Learning from image: CNN & Mono Depth

This document presents an internship project developed for Visage Technologies AD. The estimated duration of this project is six to eight week. During this period it is expected that a student will get a brief picture about vision system for vehicles, understand convolution neural networks (CNNs), how to design them, go through the learning process and analyse the output. A student should be able to read a scientific paper and get a critical overview of the work. The practical part of the project includes implementing CNN for mono depth, running training on public data set (e.g. KITTI), tuning hyper-parameters analysing results. To successfully finish the project, a student has to have good programmer skill and some understanding of machine learning. Knowledge about deep learning and CNN would be preferable, but it is not mandatory. Our mentors will be there to give you advice and discuss the literature. They will also help you organise your work and give you some useful tips through the review process.