Interaction-Aware Approach for Online Parameter Estimation of a Multi-lane Intelligent Driver Model

Buyer, Johannes; Waldenmayer, Dominic; Susmann, Nico; Zöllner, R.; Zöllner, J. Marius

doi:10.1109/itsc.2019.8917257

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Examples of particle filters used with IDM parameters include approximate online POMDP solvers [37] and fully probabilistic scene prediction algorithms [30]. The online parameter estimation approach of Buyer et al [38] is similar to ours, although they use a different IDM extension and do not use their model for forward simulation of traffic scenes [38]. None of the above models explicitly estimate "stochasticity" parameters for individual drivers.…”

Section: Rule-based Driver Models a Intelligent Driver Model And Exte...mentioning

confidence: 99%

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

Bhattacharyya¹,

Jung²,

Kruse³

et al. 2021

Preprint

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Autonomous vehicles need to model the behavior of surrounding human driven vehicles to be safe and efficient traffic participants. Existing approaches to modeling human driving behavior have relied on both data-driven and rulebased methods. While data-driven models are more expressive, rule-based models are interpretable, which is an important requirement for safety-critical domains like driving. However, rule-based models are not sufficiently representative of data, and data-driven models are yet unable to generate realistic traffic simulation due to unrealistic driving behavior such as collisions. In this paper, we propose a methodology that combines rule-based modeling with data-driven learning. While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering. We perform driver modeling experiments on the task of highway driving and merging using data from three real-world driving demonstration datasets. Our results show that driver models based on our hybrid rule-based and data-driven approach can accurately capture real-world driving behavior. Further, we assess the realism of the driving behavior generated by our model by having humans perform a "driving Turing test," where they are asked to distinguish between videos of real driving and those generated using our driver models.

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Section: Rule-based Driver Models a Intelligent Driver Model And Exte...mentioning

confidence: 99%

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

Bhattacharyya¹,

Jung²,

Kruse³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Examples of particle filters used with IDM parameters include approximate online POMDP solvers [17] and fully probabilistic scene prediction algorithms [10]. The online parameter estimation approach of Buyer et al is similar to ours, although they use a different IDM extension and do not use their model for forward simulation of traffic scenes [18]. None of the above models explicitly estimate "stochasticity" parameters for individual drivers.…”

Section: B Related Workmentioning

confidence: 99%

Online Parameter Estimation for Human Driver Behavior Prediction

Bhattacharyya

Senanayake

Brown

et al. 2020

2020 American Control Conference (ACC)

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Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealisticeven dangerous-behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.

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A Study on the Fuel-Saving Potential of a Multi-Criteria Speed Trajectory Planning for HEV

Waldenmayer,

Buyer,

Bause

et al. 2024

Proceedings

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Interaction-Aware Approach for Online Parameter Estimation of a Multi-lane Intelligent Driver Model

Cited by 11 publications

References 19 publications

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

Online Parameter Estimation for Human Driver Behavior Prediction

A Study on the Fuel-Saving Potential of a Multi-Criteria Speed Trajectory Planning for HEV

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