| Organizer |
M.Sc. Sina Shafaei |
| Modul |
IN2107 |
| Type |
MasterSeminar |
| Semester |
WS 2017/2018 |
| ECTS |
4.0 |
| SWS |
2 |
| Time & Location |
02.09.023 / 15:00-17:00 |
News
The first session of the seminar (Agenda: Details, Sessions, Team Members) took place on 20.10.2017 at 02.09.023 15:00-17:00 Slides
The preliminary talk took place on 12th of July 2017, 13:00 - 14:00 at
02.09.023 on the second floor.
Slides
Content
Challenged by the increasing complexity of today's software and physical environments
specially in the domain of autonomous driving, new technologies are required which
seamlessly integrate with driver and other occupants needs. The development of suitable
context prediction methodologies to provide the proactive behavior for the intelligent
applications, is however a challenge. The reason is that future context information, hidden
in the raw context traces left by users in the real world, is not immediately accessible
to applications. Therefore, sophisticated context prediction approaches are required that
are able to discover and mine patterns (e.g. of a driver's behavior) from observed context
history.
In this seminar various topics will be discussed which are among the state-of-the-art in the domain of context prediction and autonomous driving
Topic Assignment
| Topic |
Team Members |
Presentation (Date) |
| C- Driver Behavior Modeling For Context Prediction |
|
12.01.2018 |
| D- Challenges of Deploying Neural Nets for Context Prediction in Fully Automated Driving |
|
12.01.2018 |
| E- Neural Networks and Deep Learning in Context Prediction |
|
19.01.2018 |
| F- Context Prediction and Reinforcement Learning |
|
19.01.2018 |
| G- Approaches for Optimizing the Accuracy of the Prediction Results |
|
26.01.2018 |
| H- Limitations of Deep Learning Methods in Context Prediction |
|
26.01.2018 |
| I- Trajectory Prolongation Approach (Interpolation/Approximation) |
|
02.02.2018 |
| J- Ambient Intelligent Systems |
|
02.02.2018 |
| A- Challenges in Designing a Context Prediction Architecture |
|
09.02.2018 |
| L- Enabling Proactiveness through Context Prediction |
|
09.02.2018 |
Material
