Learning Driver Behavior Models for Predicting Urban Traffic Situations

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Alternative title: Lernen von Fahrermodellen zur Prognose urbaner Verkehrssituationen

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Dissertation, 2024

Keyword: Autonomes Fahrzeug
Fahrerassistenzsystem
Bahnplanung
Fahrerverhalten
Maschinelles Lernen
Straßenverkehr

Event: Veröffentlichung

(where): Erlangen

(who): Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

(when): 2024

Creator: Sackmann, Moritz

Contributor: Thielecke, Jörn
Markkula, Gustav

DOI: 10.25593/open-fau-633

URN: urn:nbn:de:101:1-2405081434265.278322791422

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:45 PM CET

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Associated

Sackmann, Moritz
Thielecke, Jörn
Markkula, Gustav
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Time of origin

2024

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Predicting behavior in games

Hochschulschrift

Prediction of driver behavior

Konferenzbeitrag

Driver’s Choice and System Outcomes in Congested Traffic Networks

Hochschulschrift

Contour integration models predicting human behavior

Behaviour of navigators in critical traffic situations

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Does Providing Information to Drivers Reduce Traffic Congestion?

Predicting voting behavior using digital trace data

Predicting traffic noise using land-use regression—a scalable approach

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Recognition of Traffic Situations based on Conceptual Graphs

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Risk analysis, driver behaviour and traffic safety at intersections in motorcycle-dominated traffic flow

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Learning Driver Behavior Models for Predicting Urban Traffic Situations

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Other Objects (12)

Learning Behavior Models for Interpreting and Predicting Traffic Situations

Vision-based prediction of human driver behavior in urban traffic environments

Predicting behavior in games

Prediction of driver behavior

Driver’s Choice and System Outcomes in Congested Traffic Networks

Contour integration models predicting human behavior

Behaviour of navigators in critical traffic situations

Does Providing Information to Drivers Reduce Traffic Congestion?

Predicting voting behavior using digital trace data

Predicting traffic noise using land-use regression—a scalable approach

Recognition of Traffic Situations based on Conceptual Graphs

Risk analysis, driver behaviour and traffic safety at intersections in motorcycle-dominated traffic flow

Learning Behavior Models for Interpreting and Predicting Traffic Situations

Vision-based prediction of human driver behavior in urban traffic environments

Predicting behavior in games

Prediction of driver behavior

Driver’s Choice and System Outcomes in Congested Traffic Networks

Contour integration models predicting human behavior

Behaviour of navigators in critical traffic situations

Does Providing Information to Drivers Reduce Traffic Congestion?

Predicting voting behavior using digital trace data

Predicting traffic noise using land-use regression—a scalable approach

Recognition of Traffic Situations based on Conceptual Graphs

Risk analysis, driver behaviour and traffic safety at intersections in motorcycle-dominated traffic flow

Learning Behavior Models for Interpreting and Predicting Traffic Situations

Vision-based prediction of human driver behavior in urban traffic environments

Predicting behavior in games

Prediction of driver behavior

Driver’s Choice and System Outcomes in Congested Traffic Networks

Contour integration models predicting human behavior

Behaviour of navigators in critical traffic situations

Does Providing Information to Drivers Reduce Traffic Congestion?

Predicting voting behavior using digital trace data

Predicting traffic noise using land-use regression—a scalable approach

Recognition of Traffic Situations based on Conceptual Graphs

Risk analysis, driver behaviour and traffic safety at intersections in motorcycle-dominated traffic flow

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