Social interactions for autonomous driving: A review and perspectives

W Wang, L Wang, C Zhang, C Liu… - Foundations and Trends …, 2022‏ - nowpublishers.com
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

Model predictive control for autonomous ground vehicles: A review

S Yu, M Hirche, Y Huang, H Chen… - Autonomous Intelligent …, 2021‏ - Springer
This paper reviews model predictive control (MPC) and its wide applications to both single
and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established …

Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms

R Qin, W Ding, J Li, S Guan, G Wang… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Empowered by blockchain and Web3 technologies, decentralized autonomous
organizations (DAOs) are able to redefine resources, production relations, and …

Milestones in autonomous driving and intelligent vehicles—Part I: Control, computing system design, communication, HD map, testing, and human behaviors

L Chen, Y Li, C Huang, Y **ng, D Tian… - … on Systems, Man …, 2023‏ - ieeexplore.ieee.org
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

X Di, R Shi - Transportation research part C: emerging technologies, 2021‏ - Elsevier
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 …

A multi-vehicle game-theoretic framework for decision making and planning of autonomous vehicles in mixed traffic

Y Yan, L Peng, T Shen, J Wang, D Pi… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
To improve the safety, comfort, and efficiency of the intelligent transportation system,
particularly in complex traffic environments where autonomous vehicles (AVs) and human …

Game-theoretic planning for self-driving cars in multivehicle competitive scenarios

M Wang, Z Wang, J Talbot, JC Gerdes… - IEEE Transactions on …, 2021‏ - ieeexplore.ieee.org
In this article, we propose a nonlinear receding horizon game-theoretic planner for
autonomous cars in competitive scenarios with other cars. The online planner is specifically …

Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios

K Liu, N Li, HE Tseng, I Kolmanovsky… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

Simulation of vehicle interaction behavior in merging scenarios: A deep maximum entropy-inverse reinforcement learning method combined with game theory

W Li, F Qiu, L Li, Y Zhang… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Simulation testing based on virtual scenarios can improve the efficiency of safety testing for
high-level autonomous vehicles (AVs). In most traffic scenarios, such as merging scenarios …

Aggfollower: Aggressiveness informed car-following modeling

X Chen, X Yuan, M Zhu, X Zheng… - IEEE Transactions …, 2024‏ - ieeexplore.ieee.org
Car-following is the most common driving scenario where a following vehicle follows a lead
vehicle in the same lane. One crucial factor of car-following behavior is driving style which …