Autonomous Driving an der TU München

Karteikarten und Zusammenfassungen für Autonomous Driving an der TU München

Arrow Arrow

Komplett kostenfrei

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

Lerne jetzt mit Karteikarten und Zusammenfassungen für den Kurs Autonomous Driving an der TU München.

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Important factors for AD at CES

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

CES means

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Predictions for AD in upcoming years

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

First functional robot

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Signature of robot architectures

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Unstructured environments for path planning

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Holonomic vs Nonholonomic

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

RRT - Weak completeness

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Refraction

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Reflection

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Scattering

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Absorption

Kommilitonen im Kurs Autonomous Driving an der TU München. erstellen und teilen Zusammenfassungen, Karteikarten, Lernpläne und andere Lernmaterialien mit der intelligenten StudySmarter Lernapp. Jetzt mitmachen!

Jetzt mitmachen!

Flashcard Flashcard

Beispielhafte Karteikarten für Autonomous Driving an der TU München auf StudySmarter:

Autonomous Driving

Important factors for AD at CES

Applicability : The ability of the function to operate, expressed in percent. E.g. a lane change assist that sees clear lane markings, road side boundaries, and convoy tracks, and an empty left lane, might set this to 100%; if it only sees convoy tracks and lane markings are unclear (e.g. in a construction site), it might set this to 30%; if the left lane is occupied, it will always set this to 0%. 


Desire : The desire of the function to operate, in percent. E.g. an adaptive cruise control function set to 130km/h on an empty highway might set this to 100%; if it finds itself behind a truck going 80km/h it might set this to 50%. 


Risk : A scalar, expressed in percent, giving an assessment of the risk involved when performing the behavior. A lane changing assistant that sees a perfectly clear left lane might set this to 20% (since visibility from the ego vehicle will always be obstructed), one that sees a slowly -approaching vehicle to the rear in the left lane might set this to 50%, one that sees a vehicle arriving with high difference velocity might set it to 90%. 


Comfort : A scalar, expressed in percent, giving an assessment of the comfort to the driver that performing a certain motion will entail; expected high lateral or longitudinal acceleration or deceleration will result in a low comfort level, gentle motions in a high one.

Autonomous Driving

CES means
Consumer Electronics Show

Autonomous Driving

Predictions for AD in upcoming years
2016 - Partially Automated
- Monitoring of the system required
- Driver needs to be able to take over the driving task at any moment
Example: Stop and go up to 30km/h

2020 - Highly Automated
- Monitoring of the system not required
- Driver needs to be able to take over the driving task with lead time
Example: Stop and go (highway)

2025 - Fully Automated
- Monitoring of the system not required
- Driver does not need to be able to take over the driving task
Example: Highway driving up to 130km*h

Autonomous Driving

First functional robot
Unimate 1961
- Signature: Electrical/Hydraulic engine
- Digital Control, programmable
- No communication device
- No external sensors

Autonomous Driving

Signature of robot architectures

- Many sensors with different measurement principles 

–Real time reaction necessary , short reaction times 

–Various abstraction levels , direct reaction up to highlevel planning

Autonomous Driving

Unstructured environments for path planning

Example: Parking lot without pre - defined paths 


Large search space of possible paths 


Mostly high distance to obstacles, but optimal path can lead through bottlenecks

Autonomous Driving

Holonomic vs Nonholonomic
Holonomic system where a robot can move in any direction in the configuration space.

Nonholonomic systems are systems where the velocities (magnitude and or direction) and other derivatives of the position are constraint. History of states is needed in order to determine the current state.

Autonomous Driving

RRT - Weak completeness

•Resolution complete : if no solution exists, the algorithm will run forever. 


•Probabilistically complete : with infinite samples, the probability of finding an existing solution converges to one.

Autonomous Driving

Refraction

Refraction: wave crossing from one medium into another, experiencing a change in direction, while continuing to travel through the new medium .

Autonomous Driving

Reflection

Reflection: Change in direction of a wave, between two different media, with outgoing angle equal to the incident angle on the other side of the surfaces normal.

Autonomous Driving

Scattering

Scattering: radiation such as light being forced to deviate from straight path due to localized non -uniformity in propagation medium. 


For example because of droplets or surface roughness (scattering centers)

Autonomous Driving

Absorption

Absorption: Loss of energy of propagating wave while traveling through a medium. 

e.g. conversion into thermal energy in damping material (Ultrasound: foam ; Light: carbon black ) depends on depth , absorption coefficient)

Melde dich jetzt kostenfrei an um alle Karteikarten und Zusammenfassungen für Autonomous Driving an der TU München zu sehen

Singup Image Singup Image
Wave

Andere Kurse aus deinem Studiengang

Für deinen Studiengang Autonomous Driving an der TU München gibt es bereits viele Kurse auf StudySmarter, denen du beitreten kannst. Karteikarten, Zusammenfassungen und vieles mehr warten auf dich.

Zurück zur TU München Übersichtsseite

Blockchain

Cognitive System

Databases for modern CPU

Protein Prediction I

Data Analysis in R

18WS_Strategisches_IT_Management

Patterns

Softwaretechnik

Requirements Engineering

Web Application Engineering

Business Analytics

Echtzeitsysteme

Patterns in Software Engineering

Principles of Economics

Data Mining and Knowledge Discovery

Introduction to Deep Learning

Data Mining and KD

Visual data analytics

Security Engineering

SE betr Anw

Algorithmic Game Theory

Grundlagen der Künstlichen Intelligenz

Virtual Machines

SEBA Master

Was ist StudySmarter?

Was ist StudySmarter?

StudySmarter ist eine intelligente Lernapp für Studenten. Mit StudySmarter kannst du dir effizient und spielerisch Karteikarten, Zusammenfassungen, Mind-Maps, Lernpläne und mehr erstellen. Erstelle deine eigenen Karteikarten z.B. für Autonomous Driving an der TU München oder greife auf tausende Lernmaterialien deiner Kommilitonen zu. Egal, ob an deiner Uni oder an anderen Universitäten. Hunderttausende Studierende bereiten sich mit StudySmarter effizient auf ihre Klausuren vor. Erhältlich auf Web, Android & iOS. Komplett kostenfrei. Keine Haken.

Awards

Bestes EdTech Startup in Deutschland

Awards
Awards

European Youth Award in Smart Learning

Awards
Awards

Bestes EdTech Startup in Europa

Awards
Awards

Bestes EdTech Startup in Deutschland

Awards
Awards

European Youth Award in Smart Learning

Awards
Awards

Bestes EdTech Startup in Europa

Awards