About us
Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent and affordable solutions for vehicles, machines, traffic and transportation. In 2021, Continental generated sales of EUR33.8 billion and currently employs more than 190,000 people in 58 countries and markets. On October 8, 2021, the company celebrated its 150th anniversary.
PhD Student AI – Failure Case Suppression for Perception (m/f/diverse)
(Job ID: 226678BR)

Job Description

We are searching for a research scientist (m/f/diverse) to join our artificial intelligence (AI) team in Berlin. As part of the central pre-development department, our goal is to develop and enable the use of AI in future Continental products. You will work in an innovative team of specialists and students with the goal of a Ph.D.
The traceability of an AI function’s decision is essential, especially for safety-critical systems such as autonomous driving. In this context, two aspects will be considered in this thesis. First, the development of methods for plausible AI perception function predictions by estimating uncertainties in decisions through additional input of knowledge. And second, the investigation of methods for self-explanation of predictions by extracted knowledge from the AI function with the goal to realize an assisted error correction. The goal is to develop a prototype system, which allows an interpretation of the reliability by means of metrics, as well as showing strategies for failure correction.
Consider a system with a perception function and a subsequent path planning component. The perception function is a deep neural network (DNN) that determines a semantic segmentation of images. The plausibility check of the prediction is intended to determine the uncertainty in the decision, such that this can be considered during path planning. However, since the confidence statements of DNNs are unreliable, further methods must be applied. Apart from calibration methods also knowledge, e.g., physical correlations, shall be used to gain plausible predictions.
For the self-explanation of DNN predictions, the latent features are used. For this purpose, knowledge is extracted from a DNN in the first step and finally used in the self-explanation component to increase the comprehensibility of the decision. For example, in the case of pedestrian detection, the first step could be to determine concepts (e.g., hands, torso, or head) that play a role in the decision process explaining the predictions. This case becomes interesting if a person is occluded and only certain concepts are visible. Finally, detected failure cases can be analyzed using the self-explanation and appropriate strategies shall be developed to correct them.

Goals of thesis:
Development of approaches for the plausibility check of AI perception functions decisions, by means of estimation of uncertainties using additional world knowledge and extracted concepts
Development of a self-explanation component for explaining DNN decisions

Creation of strategies to resolve failure cases using the self-explanation component

Integrating the plausibility check and self-explanation component into a simulation to run experiments and demonstrations

Development of KPIs to monitor the effectiveness of the developed methods
Working in the AI Campus in Berlin means being part of a highly motivated and growing team of researchers working in the field of artificial intelligence. Being located in one of the German centers for AI, we strive to work on the frontier of research by having open exchange with experts and highly influential players in the community. Frequent talks, gatherings, and discussions as well as formal and informal team events ensure prompt spread of information. In our freshly established team of mostly Ph.D. students, we are maintaining an harmonious atmosphere of easy-going collaboration and exchange. Despite this inviting culture of togetherness, we will provide you with enough flexibility in terms of working time and location. Doing a Ph.D. in Continental is going to equip you with exceptional qualification, network, and industrial experience in the future field of autonomous driving. We are looking forward to meet you at the campus!

Job Requirements

Studied artificial intelligence, computer science, mathematics, physics, robotics or related fields
Intensively worked with artificial-intelligence-related algorithms

Desirable to know about autonomous systems or to be interested in

Able to professionally speak and write in English language

Speaking or eager to learn German

Curious about giving new technologies a practical try

Independent, creative, and proactive working style

Actively working and communicating in a team

Applications from severely handicapped people are welcome.

What we offer
With us you can expect:
In addition to the interesting field of activity of the function, the city of Berlin offers a high recreational value and stands for quality of life
You will work in an innovative work environment, namely the „co-working space AI Campus“
Become part of our motivated team – we are looking forward to you
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