Asymmetric optimal-velocity car-following model

Xu, Xinni; Pang, John Z. F.; Monterola, Christopher

doi:10.1016/j.physa.2015.04.023

Cited by 21 publications

(6 citation statements)

References 17 publications

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“…The purpose of the analysis of AFVDM pointed out that the positive velocity difference term is significantly higher than the negative velocity difference term, which agrees well with the results from studies on vehicle mechanics. In 2015, the authors (Xu et al, 2015) interested in taking the asymmetric characteristic of the velocity differences of vehicles and they proposed an asymmetric optimal velocity model for a car-following theory (AOV). They based on the assumption that the relationship between relative velocity and acceleration (deceleration) is in general nonlinear as demonstrated by actual experiments (Shamoto et al, 2011).…”

Section: Lim mentioning

confidence: 99%

A Review Analysis of Optimal Velocity Models

Lazar

Rhoulami

Rahmani

2016

Period. Polytech. Transp. Eng.

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Section: Lim mentioning

confidence: 99%

A Review Analysis of Optimal Velocity Models

Lazar

Rhoulami

Rahmani

2016

Period. Polytech. Transp. Eng.

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“…Similarly, an autonomous vehicle should have the same prediction capability to safely navigate alongside human drivers. Some researchers addressed the vehicle trajectory prediction task, also known as microscopic traffic modeling, by building hand-crafted functions based on the available domain-knowledge to model average driving behaviors [28,11,52,10,9,56]. These methods are interpretable and usually lead to a set of feasible predictions.…”

Section: Introductionmentioning

confidence: 99%

Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

Bahari,

Nejjar,

Alahi

2021

Preprint

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Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with datadriven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, i.e., having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a "Realistic Residual Block" (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes. We will release our code and data split here: https://github.com/vita-epfl/RRB.

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“…Zheng et al investigated trajectories to compare driver behavior before and after oscillations (11). Peng et al and Xu et al proposed variations of the Optimal Velocity Model to model disturbance propagation by incorporating anticipation and asymmetric driving behaviors respectively (18,19). Similarly, various studies have used variations of the Intelligent Driver Model (IDM) to reproduce stop-andgo traffic oscillations (20)(21)(22)(23)(24)(25).…”

mentioning

confidence: 99%

Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations

Srivastava

Chen

Ahn

2020

Transportation Research Record: Journal of the Transportation R

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This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.

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Asymmetric optimal-velocity car-following model

Cited by 21 publications

References 17 publications

A Review Analysis of Optimal Velocity Models

A Review Analysis of Optimal Velocity Models

Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations

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