Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving
The last decade witnessed a great development of automated driving vehicles (ADVs) and
vehicle intelligence. The significant increment of machine intelligence poses a new …
vehicle intelligence. The significant increment of machine intelligence poses a new …
Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges
MH Alkinani, WZ Khan, Q Arshad - Ieee Access, 2020 - ieeexplore.ieee.org
Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
CitySim: a drone-based vehicle trajectory dataset for safety-oriented research and digital twins
O Zheng, M Abdel-Aty, L Yue… - Transportation …, 2024 - journals.sagepub.com
The development of safety-oriented research and applications requires fine-grain vehicle
trajectories that not only have high accuracy, but also capture substantial safety-critical …
trajectories that not only have high accuracy, but also capture substantial safety-critical …
Human-like decision making for autonomous driving: A noncooperative game theoretic approach
Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
MESON: A mobility-aware dependent task offloading scheme for urban vehicular edge computing
Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing
(MEC) in road scenarios. One key technology of VEC is task offloading, which allows …
(MEC) in road scenarios. One key technology of VEC is task offloading, which allows …
An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle
W Wang, T Qie, C Yang, W Liu… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
In the future complex intelligent transportation environments, lane-changing behavior of
surrounding vehicles is a significant factor affecting the driving safety. It is necessary to …
surrounding vehicles is a significant factor affecting the driving safety. It is necessary to …
A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction
Financial time series prediction is a hot topic in machine learning field, but existing works
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
An interacting multiple model for trajectory prediction of intelligent vehicles in typical road traffic scenario
This article presents an interacting multiple model (IMM) for short-term prediction and long-
term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics …
term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics …