Car-Following Models: A Multidisciplinary Review
- URL: http://arxiv.org/abs/2304.07143v4
- Date: Tue, 5 Mar 2024 16:50:50 GMT
- Title: Car-Following Models: A Multidisciplinary Review
- Authors: Tianya Terry Zhang, Ph.D., Peter J. Jin, Ph.D., Sean T. McQuade,
Ph.D., Alexandre Bayen, Ph.D., Benedetto Piccoli
- Abstract summary: Car-following models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, and reinforcement learning.
It reviews representative algorithms, ranging from theory-based kinematic models, Psycho-Physical Models, and Adaptive cruise control models to data-driven algorithms like Reinforcement Learning (RL) and Imitation Learning (IL)
- Score: 35.57095196826516
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Car-following (CF) algorithms are crucial components of traffic simulations
and have been integrated into many production vehicles equipped with Advanced
Driving Assistance Systems (ADAS). Insights from the model of car-following
behavior help us understand the causes of various macro phenomena that arise
from interactions between pairs of vehicles. Car-following models encompass
multiple disciplines, including traffic engineering, physics, dynamic system
control, cognitive science, machine learning, and reinforcement learning. This
paper presents an extensive survey that highlights the differences,
complementarities, and overlaps among microscopic traffic flow and control
models based on their underlying principles and design logic. It reviews
representative algorithms, ranging from theory-based kinematic models,
Psycho-Physical Models, and Adaptive cruise control models to data-driven
algorithms like Reinforcement Learning (RL) and Imitation Learning (IL). The
manuscript discusses the strengths and limitations of these models and explores
their applications in different contexts. This review synthesizes existing
researches across different domains to fill knowledge gaps and offer guidance
for future research by identifying the latest trends in car following models
and their applications.
Related papers
- Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities [89.40778301238642]
Model merging is an efficient empowerment technique in the machine learning community.
There is a significant gap in the literature regarding a systematic and thorough review of these techniques.
arXiv Detail & Related papers (2024-08-14T16:58:48Z) - 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) - Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives [56.2139730920855]
We present a systematic analysis of MM-VUFMs specifically designed for road scenes.
Our objective is to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques.
We provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models.
arXiv Detail & Related papers (2024-02-05T12:47:09Z) - IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence
Car-Following Trajectory Prediction [24.94160059351764]
Most car-following models are generative and only consider the inputs of the speed, position, and acceleration of the last time step.
We implement a novel structure with two independent encoders and a self-attention decoder that could sequentially predict the following trajectories.
Numerical experiments with multiple settings on simulation and NGSIM datasets show that the IDM-Follower can improve the prediction performance.
arXiv Detail & Related papers (2022-10-20T02:24:27Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Bidirectional Interaction between Visual and Motor Generative Models
using Predictive Coding and Active Inference [68.8204255655161]
We propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories.
We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories.
arXiv Detail & Related papers (2021-04-19T09:41:31Z) - A Physics-Informed Deep Learning Paradigm for Car-Following Models [3.093890460224435]
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.
arXiv Detail & Related papers (2020-12-24T18:04:08Z) - A Taxonomy and Review of Algorithms for Modeling and Predicting Human
Driver Behavior [36.80532606715206]
We present a review and taxonomy of 200 models from the literature on driver behavior modeling.
We begin by introducing a mathematical framework for describing the dynamics of interactive multi-agent traffic.
Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction.
arXiv Detail & Related papers (2020-06-15T23:53:45Z) - Survey of Deep Reinforcement Learning for Motion Planning of Autonomous
Vehicles [0.0]
Article describes one of these fields, Deep Reinforcement Learning (DRL)
Paper describes vehicle models, simulation possibilities and computational requirements.
Surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving.
arXiv Detail & Related papers (2020-01-30T09:47:22Z)
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.