TU Darmstadt / ULB / TUprints

Modeling Driving Behavior of Human Drivers for Trajectory Planning

Ziegler, Christoph ; Willert, Volker ; Adamy, Jürgen (2022):
Modeling Driving Behavior of Human Drivers for Trajectory Planning. (Postprint)
In: IEEE Transactions on Intelligent Transportation Systems, IEEE, ISSN 1524-9050, e-ISSN 1558-0016,
DOI: 10.26083/tuprints-00021613,
[Article]

[img] Text
TITS3183204_accepted_version.pdf
Copyright Information: In Copyright.

Download (2MB)
Item Type: Article
Origin: Secondary publication
Status: Postprint
Title: Modeling Driving Behavior of Human Drivers for Trajectory Planning
Language: English
Abstract:

Extracted driving behavior of human driven vehicles can benefit the development of various applications like trajectory prediction or planning, abnormal driving detection, driving behavior classification, traffic simulation modeling, etc. In this paper, we focus on modeling human driving behavior in order to find simplifications for trajectory planning. Using a time-discrete kinematic bicycle model with the vehicle’s acceleration and steering rate as inputs, we model the human driven trajectories of an urban intersection drone dataset for different input sampling times. While most planning algorithms are using input sampling times below 0.33 s, we are able to model 98.2 % of the human driven trajectories of the investigated dataset with a sampling time of 0.6 s. Using longer input sampling times can result in smoother trajectories and longer planning horizons, and thus more efficient trajectories. In a next step, we analyze the correlations between the input of our model and the current state/last input. Such a priori knowledge could simplify common planning algorithms like model predictive control or tree-search based planners by limiting the action space of the ego-vehicle. We propose nonlinear transformations for steering rate and steering angle to represent correlations between speed, acceleration, steering angle and steering rate. In the transformed space the statistics are very well modeled by multivariate Gaussian distributions. Using a multivariate Gaussian, a fast usable behavior model is extracted which is independent of the environment.

Journal or Publication Title: IEEE Transactions on Intelligent Transportation Systems
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 10 Seiten
Uncontrolled Keywords: Driving behavior, sampling time, sampling rate, automated vehicles, kinematic bicycle model, statistics, trajectory planning, urban driving
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 08 Jul 2022 12:11
Last Modified: 08 Jul 2022 12:11
DOI: 10.26083/tuprints-00021613
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-216136
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21613
PPN:
Export:
Actions (login required)
View Item View Item