Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
[HTML][HTML] Applications of deep learning in congestion detection, prediction and alleviation: A survey
Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger datasets of higher …
service of the transportation network. With increasing access to larger datasets of higher …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
[HTML][HTML] DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …
management in an urban road network. How to build an effective large-scale traffic state …
STDEN: Towards physics-guided neural networks for traffic flow prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent
Transportation System, is a long-standing but still challenging task for industrial and …
Transportation System, is a long-standing but still challenging task for industrial and …
When physics meets machine learning: A survey of physics-informed machine learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …
of physics, which is the high level abstraction of natural phenomenons and human …
A physics-informed deep learning paradigm for car-following models
Car-following behavior has been extensively studied using physics-based models, such as
Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena …
Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena …
A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation
Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …
[HTML][HTML] TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI
Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve
human living quality across the lifespan, which enables assistance of movement and …
human living quality across the lifespan, which enables assistance of movement and …
Physics-informed deep learning for traffic state estimation based on the traffic flow model and computational graph method
Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However,
it is extremely challenging because the traffic data quality is often affected by the installation …
it is extremely challenging because the traffic data quality is often affected by the installation …