Autonomous Driving

Fig. 1: Track in the Lab im Labor Autonome Systeme

Autor:	TBD
Art:	PS, PA, BA, MA
Starttermin:	TBD
Abgabetermin:	TBD
Betreuer:	Prof. Dr.-Ing. Schneider

Introduction

A model car (scale 1:20) equipped with a different sensors should

• drive autonomously in the right lane,
• avoid obstacles,
• react on sign and crossroads and
• parc autonomously.

Usually this can done by conventional algorithms (image processing, LiDAR object detection and sensor data fusion) or artificial intelligence (AI) (see Fig. 1). Choose a topic, analyze the possible solutions an solve t

Task list

1. Familiarization with the topic
   1. lane keeping
   2. avoid obstacles
   3. traffic sign detection
   4. right of way at intersections
   5. autonomous parking
2. Set up requirements for the system
3. Research on solutions for the task
4. Evaluation of the solutions using a morphological box (Zwicky box)
5. Implementation the most promising solution with MATLAB^®
6. Evaluate the results based on technical features
7. Discussion of the results
8. Testing of the system requirements - proof of functionality
9. 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:

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