Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
Multi-agent reinforcement learning for autonomous vehicles: A survey
In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles
The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have
catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In …
catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In …
Improving the generalizability and robustness of large-scale traffic signal control
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals.
Compared to traditional approaches, RL approaches can learn from higher-dimensionality …
Compared to traditional approaches, RL approaches can learn from higher-dimensionality …
Shared learning of powertrain control policies for vehicle fleets
Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-
the-field learning of powertrain control policies that optimize fuel economy and other …
the-field learning of powertrain control policies that optimize fuel economy and other …
A Comprehensive Review on Deep Learning-Based Motion Planning and End-To-End Learning for Self-Driving Vehicle
M Ganesan, S Kandhasamy, B Chokkalingam… - IEEE …, 2024 - ieeexplore.ieee.org
Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber,
and Tesla investing significantly in self-driving technology. These vehicles could transform …
and Tesla investing significantly in self-driving technology. These vehicles could transform …
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
The rise of multi-agent systems, especially the success of multi-agent reinforcement learning
(MARL), is reshaping our future across diverse domains like autonomous vehicle networks …
(MARL), is reshaping our future across diverse domains like autonomous vehicle networks …
Graph-based multi agent reinforcement learning for on-ramp merging in mixed traffic
D Xu, B Zhang, Q Qiu, H Li, H Guo, B Wang - Applied Intelligence, 2024 - Springer
Abstract The application of Deep Reinforcement Learning (DRL) has significantly impacted
the development of autonomous driving technology in the field of intelligent transportation …
the development of autonomous driving technology in the field of intelligent transportation …