Autonomous Road Surveying: Unterschied zwischen den Versionen
Zur Navigation springen
Zur Suche springen
Keine Bearbeitungszusammenfassung |
|||
| (19 dazwischenliegende Versionen desselben Benutzers werden nicht angezeigt) | |||
| Zeile 15: | Zeile 15: | ||
{| | {| | ||
= | = Introduction = | ||
A model car (scale 1:20) equipped with a camera should drive autonomously in the right lane. Usually this is done by image processing. In this bachelor thesis steering angle and video are used to train an artificial intelligence (AI) to drive autonomously on the track (see Fig. 1) | A model car (scale 1:20) equipped with a camera should drive autonomously in the right lane. Usually this is done by image processing. In this bachelor thesis steering angle and video are used to train an artificial intelligence (AI) to drive autonomously on the track (see Fig. 1) | ||
= | = Task list = | ||
# Familiarization with the topic | # Familiarization with the topic | ||
# | # Set up requirements for the system | ||
# Research on solutions for the task | |||
# Implementation in MATLAB<sup>®</sup>. Teach a AI to drive in the right lane. Input are video and steering angle | |||
# | # Evaluation of the solutions using a morphological box ([https://en.wikipedia.org/wiki/Morphological_box Zwicky box) | ||
# | # Implementation the most promising on the [[JetRacer]] | ||
# | # CAD design and 3D printing of prism mount for the [[JetRacer]] | ||
# | # Prism assembly and recording of the vehicle movement | ||
# Evaluate the results based on technical features | |||
# Discussion of the results | # Discussion of the results | ||
# | # Testing of the system requirements - proof of functionality | ||
# | # Scientific documentation as a wiki article with an animated gif | ||
= Knowledge Requirements = | |||
The project requires prior knowledge in some but not all of the following subject areas. If you do not meet the requirements, the task can be individually adapted based on your previous knowledge. | The project requires prior knowledge in some but not all of the following subject areas. If you do not meet the requirements, the task can be individually adapted based on your previous knowledge. | ||
* Model making (e.g. woodworking, metalworking, CAD, 3D printing) | * Model making (e. g. woodworking, metalworking, CAD, 3D printing) | ||
* MATLAB<sup>®</sup>/Simulink | * MATLAB<sup>®</sup>/Simulink | ||
* Robotics | * Robotics | ||
* Control technology | * Control technology | ||
* Document versioning with SVN | * Document versioning with SVN | ||
* Documentation with Word and in the HSHL Wiki. | * Documentation with Word and in the HSHL Wiki. | ||
= Requirements for scientific work = | |||
* Scientific approach (project plan, etc.), useful article: [[Gantt-Diagramm| Create Gantt chart]] | * Scientific approach (project plan, etc.), useful article: [[Gantt-Diagramm| Create Gantt chart]] | ||
* Weekly progress reports (informative), update the table [[Diskussion: | * Weekly progress reports (informative), update the table [[Diskussion:Autonomous_Road_Surveying|Meeting Minutes]] in conversation with Prof. Schneider | ||
* | * Project presentation in the wiki | ||
* | * Daily backup of work results in SVN | ||
*[[Studentische_Arbeiten_bei_Prof._Schneider|Student work with Prof. Schneider]] | |||
*[[Studentische_Arbeiten_bei_Prof._Schneider| | *[[Anforderungen_an_eine_wissenschaftlich_Arbeit| Requirements for a scientific work]] | ||
*[[Anforderungen_an_eine_wissenschaftlich_Arbeit| | = Getting started = | ||
* [[Lane_Keeping_with_AI| Wiki article: Lane Keeping with AI]] | |||
* [https://de.mathworks.com/videos/deploying-deep-neural-networks-to-gpus-and-cpus-using-matlab-coder-and-gpu-coder-1567105707114.html?s_tid=abt_vid_pers_recs_nonen Deploying Deep Neural Networks to GPUs and CPUs Using MATLAB Coder and GPU Coder] | |||
* [https://de.mathworks.com/videos/matlab-and-simulink-robotics-arena-deep-learning-with-nvidia-jetson-and-ros--1542015526909.html Deep Learning with MATLAB, NVIDIA Jetson, and ROS] | |||
* [https://de.mathworks.com/matlabcentral/answers/2072946-matlab-to-control-jetracer-jetson-nano-tx1-motor/?s_tid=ans_lp_feed_leaf FAQ: Matlab to control jetracer(jetson nano tx1) motor] | |||
* [[JetRacer| Wiki article: JetRacer]] | |||
* [https://github.com/NVIDIA-AI-IOT/jetracer NVidia: JetRacer] | |||
* [https://www.formulaedge.org/what-is-jetracer formulaedge.org] | |||
* [https://arxiv.org/html/2404.06229v1 Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development] | |||
* [https://pypi.org/project/lazypredict/ Diese Library ist aus unserer Erfahrung ganz gut, um erst einmal den vielversprechendsten Algorithmus für die Test-Daten zu finden] | |||
* S<span style="font-variant:small-caps">chreiber</span>, C.: ''KI-gestützte „Follow-Me“-Funktion am Beispiel des JetRacer''. Mittweida, Hochschule Mittweida – University of Applied Sciences, Fakultät Ingenieurwissenschaften, Masterarbeit, 2023. URL: [https://monami.hs-mittweida.de/frontdoor/deliver/index/docId/14754/file/MA_55114_Christian-Schreiber_geschwaerzt.pdf] | |||
* [https://www.thinkautonomous.ai/blog/lane-detection/ Lane Detection: The 3 types of Deep Learning (non-OpenCV) algorithms] | |||
* [https://www.irjmets.com/uploadedfiles/paper//issue_6_june_2023/42644/final/fin_irjmets1688121553.pdf Automatic Lane Line Detection using AI] | |||
* [https://www.springerprofessional.de/en/lane-detection-in-autonomous-vehicles-using-ai/25954814 Lane Detection in Autonomous Vehicles Using AI] | |||
* [https://hbz-hhl.primo.exlibrisgroup.com/permalink/49HBZ_HHL/pmh7h/alma991001255230906482 Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology.] | |||
= Repository = | |||
* Video: | |||
* Steering angle: | |||
---- | ---- | ||
→ zurück zum Hauptartikel: [[Offene_Themen_von_Prof._Schneider| Studentische Arbeiten]] | → zurück zum Hauptartikel: [[Offene_Themen_von_Prof._Schneider| Studentische Arbeiten]] | ||
Aktuelle Version vom 28. Februar 2025, 08:42 Uhr
| Autor: | Qamar Sajjad |
| Art: | Bachelorarbeit/bachelor thesis |
| Starttermin: | TBD |
| Abgabetermin: | TBD |
| Betreuer: | Prof. Dr.-Ing. Schneider |
Introduction
A model car (scale 1:20) equipped with a camera should drive autonomously in the right lane. Usually this is done by image processing. In this bachelor thesis steering angle and video are used to train an artificial intelligence (AI) to drive autonomously on the track (see Fig. 1)
Task list
- Familiarization with the topic
- Set up requirements for the system
- Research on solutions for the task
- Implementation in MATLAB®. Teach a AI to drive in the right lane. Input are video and steering angle
- Evaluation of the solutions using a morphological box ([https://en.wikipedia.org/wiki/Morphological_box Zwicky box)
- Implementation the most promising on the JetRacer
- CAD design and 3D printing of prism mount for the JetRacer
- Prism assembly and recording of the vehicle movement
- Evaluate the results based on technical features
- Discussion of the results
- Testing of the system requirements - proof of functionality
- Scientific documentation as a wiki article with an animated gif
Knowledge Requirements
The project requires prior knowledge in some but not all of the following subject areas. If you do not meet the requirements, the task can be individually adapted based on your previous knowledge.
- Model making (e. g. woodworking, metalworking, CAD, 3D printing)
- MATLAB®/Simulink
- Robotics
- Control technology
- Document versioning with SVN
- Documentation with Word and in the HSHL Wiki.
Requirements for scientific work
- Scientific approach (project plan, etc.), useful article: Create Gantt chart
- Weekly progress reports (informative), update the table Meeting Minutes in conversation with Prof. Schneider
- Project presentation in the wiki
- Daily backup of work results in SVN
- Student work with Prof. Schneider
- Requirements for a scientific work
Getting started
- Wiki article: Lane Keeping with AI
- Deploying Deep Neural Networks to GPUs and CPUs Using MATLAB Coder and GPU Coder
- Deep Learning with MATLAB, NVIDIA Jetson, and ROS
- FAQ: Matlab to control jetracer(jetson nano tx1) motor
- Wiki article: JetRacer
- NVidia: JetRacer
- formulaedge.org
- Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development
- Diese Library ist aus unserer Erfahrung ganz gut, um erst einmal den vielversprechendsten Algorithmus für die Test-Daten zu finden
- Schreiber, C.: KI-gestützte „Follow-Me“-Funktion am Beispiel des JetRacer. Mittweida, Hochschule Mittweida – University of Applied Sciences, Fakultät Ingenieurwissenschaften, Masterarbeit, 2023. URL: [1]
- Lane Detection: The 3 types of Deep Learning (non-OpenCV) algorithms
- Automatic Lane Line Detection using AI
- Lane Detection in Autonomous Vehicles Using AI
- Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology.
Repository
- Video:
- Steering angle:
→ zurück zum Hauptartikel: Studentische Arbeiten