Authors:
Stefanie Marker, Teresa Rock, Thomas Bleher, Mohammad Bahram
Keywords:
Intelligent Vehicles, Intention Prediction, Microscopic Traffic Simulation, Agent Models, neural networks
Abstract:
Rock T.; Marker S.; Bleher T. and Bahram M. Data-Driven Prediction of Other Road Users’ Intention for Better Scene Understanding in Traffic Agents In: Proceedings of the Driving Simulation Conference 2022 Europe VR, Driving Simulation Association, Strasbourg, France, 2022, pp. 9-16
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@inproceedings{Rock2022,
title = {Data-Driven Prediction of Other Road Users’ Intention for Better Scene Understanding in Traffic Agents},
author = {Teresa Rock and Stefanie Marker and Thomas Bleher and Mohammad Bahram},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet},
year = {2022},
date = {2022-09-15},
booktitle = {Proceedings of the Driving Simulation Conference 2022 Europe VR},
pages = {9-16},
address = {Strasbourg, France},
organization = {Driving Simulation Association},
abstract = {Driving simulation in urban traffic reveals new challenges in terms of modelling surrounding road users, since the driver has to interact with artificially modelled traffic agents. Current agent models are mostly based on heuristic modelling approaches, which makes them only partially suitable for complex urban traffic, because interactive behaviour requires a higher level of situational understanding. In order to enable future agent models to behave interactively, anticipating the intention of others is required to create a better scene understanding. Predicting the intention of other road users is a frequently addressed challenge in autonomous driving. However, due to the high complexity of urban traffic, there is still a scientific gap regarding the ability to make accurate predictions in unknown or complex interactive situations. This paper addresses the challenge of enabling datadriven prediction models to perform in scenes far away from the training data, by using prior knowledge to create
semantic features, describing various complex traffic situations on a common basis. By this, data-driven models are able to capture complex behavioural patterns and transfer the learned dependencies to new situations.},
keywords = {Agent Models, intelligent vehicles, Intention Prediction, Microscopic Traffic Simulation, neural networks},
}
Download .bib file
TY - CONF
TI - Data-Driven Prediction of Other Road Users’ Intention for Better Scene Understanding in Traffic Agents
AU - Rock, Teresa
AU - Marker, Stefanie
AU - Bleher, Thomas
AU - Bahram, Mohammad
C1 - Strasbourg, France
C3 - Proceedings of the Driving Simulation Conference 2022 Europe VR
DA - 2022/09/15
PY - 2022
SP - 9
EP - 16
LA - en-US
PB - Driving Simulation Association
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2022/data-driven-prediction-of-other-road-users-intention-for-better-scene-understanding-in-traffic-agents
ER -
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