A Generic Framework for Motion Prediction in Autonomous Driving

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Alternative title: Ein generisches Framework zur Bewegungsprädiktion für Autonomes Fahren

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: München, Technische Universität München, Dissertation, 2024

Keyword: Autonomes Fahrzeug
Objektverfolgung
Fahrerassistenzsystem
Datenfusion
Objekterkennung
Bahnplanung

Event: Veröffentlichung

(where): München

(who): Universitätsbibliothek der TU München

(when): 2024

Creator: Karle, Phillip Jonathan

Contributor: Lienkamp, Markus
Peters, Steven
Lienkamp, Markus

URN: urn:nbn:de:bvb:91-diss-20240910-1739133-1-9

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:56 PM CET

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Associated

Karle, Phillip Jonathan
Lienkamp, Markus
Peters, Steven
Universitätsbibliothek der TU München

Time of origin

2024

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A Generic Framework for Motion Prediction in Autonomous Driving

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Other Objects (12)

Behavior prediction for autonomous driving using graph neural networks

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Autonomous vehicles and autonomous driving in freight transport

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Using Deep Neural Networks for Scene Understanding and Behaviour Prediction in Autonomous Driving

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

Behavior prediction for autonomous driving using graph neural networks

Perception and Prediction of Urban Traffic Scenarios for Autonomous Driving

State Lattice-based Motion Planning for Autonomous On-Road Driving

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Special issue autonomous driving

Security in autonomous driving

Long-term prediction using grid based environment models for urban autonomous driving

Autonomous vehicles and autonomous driving in freight transport

Visual Perception for Autonomous Driving

Sound Processing for Autonomous Driving

Using Deep Neural Networks for Scene Understanding and Behaviour Prediction in Autonomous Driving

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

Behavior prediction for autonomous driving using graph neural networks

Perception and Prediction of Urban Traffic Scenarios for Autonomous Driving

State Lattice-based Motion Planning for Autonomous On-Road Driving

Multimodal interaction-aware motion prediction for autonomous street crossing

Special issue autonomous driving

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Long-term prediction using grid based environment models for urban autonomous driving

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