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 …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field
Autonomous driving of multi-lane vehicle platoons have attracted significant attention in
recent years due to their potential to enhance the traffic-carrying capacity of the roads and …
recent years due to their potential to enhance the traffic-carrying capacity of the roads and …
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 …
Joint optimization of sensing, decision-making and motion-controlling for autonomous vehicles: A deep reinforcement learning approach
The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …
controlling, have been studied separately in most existing works on autonomous driving …
Deep convolutional neural network architecture design as a bi-level optimization problem
During the last decade, deep neural networks have shown a great performance in many
machine learning tasks such as classification and clustering. One of the most successful …
machine learning tasks such as classification and clustering. One of the most successful …
[HTML][HTML] A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations
T **a, H Chen - Sensors, 2024 - mdpi.com
Autonomous vehicles are rapidly advancing and have the potential to revolutionize
transportation in the future. This paper primarily focuses on vehicle motion trajectory …
transportation in the future. This paper primarily focuses on vehicle motion trajectory …
RACE: Reinforced cooperative autonomous vehicle collision avoidance
With the rapid development of autonomous driving, collision avoidance has attracted
attention from both academia and industry. Many collision avoidance strategies have …
attention from both academia and industry. Many collision avoidance strategies have …
B-gap: Behavior-rich simulation and navigation for autonomous driving
We address the problem of ego-vehicle navigation in dense simulated traffic environments
populated by road agents with varying driver behaviors. Navigation in such environments is …
populated by road agents with varying driver behaviors. Navigation in such environments is …