A Physics-Informed Deep Learning Paradigm for Car-Following Models
- URL: http://arxiv.org/abs/2012.13376v2
- Date: Fri, 25 Dec 2020 22:56:35 GMT
- Title: A Physics-Informed Deep Learning Paradigm for Car-Following Models
- Authors: Zhaobin Mo, Xuan Di, Rongye Shi
- Abstract summary: We develop a family of neural network based car-following models informed by physics-based models.
Two types of PIDL-CFM problems are studied, one to predict acceleration only and the other to jointly predict acceleration and discover model parameters.
The results demonstrate the superior performance of neural networks informed by physics over those without.
- Score: 3.093890460224435
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Car-following behavior has been extensively studied using physics-based
models, such as the Intelligent Driver Model. These models successfully
interpret traffic phenomena observed in the real-world but may not fully
capture the complex cognitive process of driving. Deep learning models, on the
other hand, have demonstrated their power in capturing observed traffic
phenomena but require a large amount of driving data to train. This paper aims
to develop a family of neural network based car-following models that are
informed by physics-based models, which leverage the advantage of both
physics-based (being data-efficient and interpretable) and deep learning based
(being generalizable) models. We design physics-informed deep learning for
car-following (PIDL-CF) architectures encoded with two popular physics-based
models - IDM and OVM, on which acceleration is predicted for four traffic
regimes: acceleration, deceleration, cruising, and emergency braking. Two types
of PIDL-CFM problems are studied, one to predict acceleration only and the
other to jointly predict acceleration and discover model parameters. We also
demonstrate the superior performance of PIDL with the Next Generation
SIMulation (NGSIM) dataset over baselines, especially when the training data is
sparse. The results demonstrate the superior performance of neural networks
informed by physics over those without. The developed PIDL-CF framework holds
the potential for system identification of driving models and for the
development of driving-based controls for automated vehicles.
Related papers
- Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach [12.08072226345806]
This study proposes physics-informed deep learning (SPIDL) for traffic state estimation.
The main contribution of SPIDL lies in addressing the "overly centralized guidance" caused by the one-to-one speed-density relationship in deterministic models during neural network training.
Experiments on the real-world dataset indicate that proposed SPIDL models achieve accurate traffic state estimation in sparse data scenarios.
arXiv Detail & Related papers (2024-09-01T07:34:40Z) - Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments [16.587883982785]
The continual evolution of autonomous driving technology requires car-following models that can adapt to diverse and dynamic traffic environments.
Traditional learning-based models often suffer from performance degradation when encountering unseen traffic patterns due to a lack of continual learning capabilities.
This paper proposes a novel car-following model based on continual learning that addresses this limitation.
arXiv Detail & Related papers (2024-07-17T06:32:52Z) - MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
We propose an adaptable personalized car-following framework - MetaFollower.
We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events.
We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
arXiv Detail & Related papers (2024-06-23T15:30:40Z) - Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
We present SIM-FSVGD for learning robot dynamics from data.
We use low-fidelity physical priors to regularize the training of neural network models.
We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
arXiv Detail & Related papers (2024-03-25T11:29:32Z) - RACER: Rational Artificial Intelligence Car-following-model Enhanced by
Reality [51.244807332133696]
This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.
Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.
RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
arXiv Detail & Related papers (2023-12-12T06:21:30Z) - A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction [5.7215490229343535]
PERL integrates the strengths of physics-based and data-driven methods for traffic state prediction.
It preserves the interpretability inherent to physics-based models and has reduced data requirements.
PERL achieves better prediction with a small dataset, compared to the physics model, data-driven model, and PINN model.
arXiv Detail & Related papers (2023-09-26T21:41:45Z) - STDEN: Towards Physics-Guided Neural Networks for Traffic Flow
Prediction [31.49270000605409]
The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field.
We propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework.
Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin.
arXiv Detail & Related papers (2022-09-01T04:58:18Z) - Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored.
We propose a new method combining both methodologies based on a new Neural Differential Equation model.
Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
arXiv Detail & Related papers (2022-07-21T12:11:18Z) - Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate
Model Predictive Trajectory Tracking [76.27433308688592]
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation.
We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience.
Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
arXiv Detail & Related papers (2022-06-07T13:51:35Z) - A Physics-Informed Deep Learning Paradigm for Traffic State Estimation
and Fundamental Diagram Discovery [3.779860024918729]
This paper contributes an improved paradigm, called physics-informed deep learning with a fundamental diagram learner (PIDL+FDL)
PIDL+FDL integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity.
We demonstrate the use of PIDL+FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation.
arXiv Detail & Related papers (2021-06-06T14:54:32Z) - A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
We propose a neural network model based on trajectories information for driving behavior recognition.
We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
arXiv Detail & Related papers (2021-03-01T06:47:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.