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1		-[REAKT] Project REAKTOR
	1	+[REAKT] Projecting Irregular Vehicle Positions on Tracks

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1		-Theses.Current Theses.WebHome
	1	+Theses.Topics for Student Theses.WebHome

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1		-The REAKT project (https://reakt.sh/ & https://www.schiene-m-l.de/) aims at developing new mobility concepts to reactivate rural rail lines. This project will evolve around developing an autonomous on-demand rail vehicle to flexibly provide service on single track lines. The **REAKTOR**. A prototype will be build in 1:32 scale, as well as a full scale version for the railway track Malente-Lütjenburg.
	1	+The REAKT DATA project (<https://reakt.sh/>) includes visualizing rail vehicle positions on the track based on a set of position data from GNNS trackers. These position data are sparse and include errors and variation, yet it can be safely assumed that the rail vehicle will not leave the track in normal operation. Hence, this topic will investigate an algorithm to process the data and project the position of the vehicles onto the track.
2	2
3		-[[image:image-20240912102452-3.jpeg||height="219" width="292"]][[image:image-20240912102840-4.jpeg||height="217" width="264"]]
	3	+![[position.png|position.png]]
4	4
5		-//(left) a railbike, the foundation of the upcoming autonomous draisine & (right) a stripped down [[LGB>>https://www.lgb.de/lp/20/willkommen-bei-lgb]] engine in for a 45mm model track.//
	5	+# Goals
6	6
7		-== Overview of topics (WIP) ==
	7	+* Develop and implement an algorithm for computing a best-effort projection of a vehicle position on a track
	8	+* Evaluate the algorithm (and potential variants of the algorithm) with the real-world data collected on the Malente-Lütjenburg track
8	8
9		-All solutions should be scalable for both demonstrators!
	10	+#### Consiterations for the Algorithm
10	10
11		-~1. An end-user app and management system for on-demand train service [Maybe split into two topics]
	12	+* Errors in GNNS data
	13	+* Gaps in GNNS data (missing network)
	14	+* Different accuracy of tracking devices (Smartphones vs. dedicated Trackers)
	15	+* Showing estimates of expected movement or potential location areas
	16	+* Handling inconsistencies
	17	+ * Unrealistic position jumps
	18	+ * Passing other vehicles (quickly) on single-lane track
	19	+ * Vehicles changing direction without stopping
12	12
13		-* App to call a train to a desired location and communicate desired destination
14		-* Locations drawn from GNSS and appropriate abstraction in 1:32 scale
15		-* Management of multiple trains on a single track line
16		-* Schedules for cooperative passenger pick up
17		-* Provisions for passenger transfer on open track
18		-* Either based on a central or decentralized communication concept
	21	+# Scope
19	19
20		-2. An autonomous train controller with risk analysis using STPA
	23	+Bachelor's Thesis
21	21
22		-* Control of an autonomous passenger train model (conceptually working for both demonstrators)
23		-* Capability for passenger transfer on open track (safe docking procedure)
24		-* Risk analysis for the controller using STPA
25		-* Safe behavior model generation using PASTA?
26		-* Assumes preprocessed sensor input and destination determination (see other topics)
	25	+# Related Work/Literature
27	27
28		-3. AI-based image recognition for autonomous train control [In cooperation with AG Distributed Systems]
	27	+*
29	29
30		-* Sensor processing of a train mounted camera
31		-* Obstacle detection at speeds up to 50 km/h
32		-* [Optional] Distance measuring using multiple cameras
	29	+[https://reakt.sh/](https://reakt.sh/) *
33	33
34		-4. AI-based sensor processing for autonomous train control [In cooperation with AG Distributed Systems]
	31	+[https://github.com/kieler/RailTrail/wiki/Data-processing#computation-of-vehicle-position-speed-and-heading](https://github.com/kieler/RailTrail/wiki/Data-processing#computation-of-vehicle-position-speed-and-heading)
35	35
36		-* Sensor processing of train mounted LiDAR or ultrasonic sensor
37		-* Obstacle detection at speeds up to 50 km/h
38		-* Distance measuring (ultrasonic sensor)
	33	+# Involved Languages/Technologies
39	39
40		-5. A digital twin for an autonomous on-demand train service [Tight interfacing with other topics]
	35	+* Python
41	41
42		-* A digital twin for an autonomous passenger train
43		-* Monitoring system for the state and location of the vehicle
44		-* Remote control capabilities
45		-* Monitoring and economic analysis of on-demand service operation [Integration of management system]
46		-* Reliability analysis to ensure transparency of autonomous operation
	37	+# Supervised by
47	47
48		-== Goals ==
49		-
50		-* TBA for each topic individually
51		-
52		-== Scope ==
53		-
54		-Bachelor's or Master's Thesis, with varying requirements to scientific scope.
55		-
56		-== Related Work/Literature ==
57		-
58		-* https://reakt.sh/
59		-* [[https:~~/~~/www.schiene-m-l.de/>>https://www.schiene-m-l.de/)]]
60		-
61		-== Involved Languages/Technologies ==
62		-
63		-* TBA for each topic individually
64		-
65		-== Supervised by ==
66		-
67	67	Alexander Schulz-Rosengarten
68		-[[als@informatik.uni-kiel.de>>mailto:als@informatik.uni-kiel.de]]
	40	+[[als@informatik.uni-kiel.de|path:mailto:als@informatik.uni-kiel.de]]

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