[HTML][HTML] Autonomous driving architectures: insights of machine learning and deep learning algorithms
MR Bachute, JM Subhedar - Machine Learning with Applications, 2021 - Elsevier
Abstract Research in Autonomous Driving is taking momentum due to the inherent
advantages of autonomous driving systems. The main advantage being the disassociation …
advantages of autonomous driving systems. The main advantage being the disassociation …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
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 …
Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning
The advanced cruise control system has expanded the energy-saving potential of the hybrid
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey
The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …
the significant advances in communication and computing paradigms, which provide a safer …
Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels
In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …
(AVs) decision-making. However, the deployment of these technologies is usually …
A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge
Ride comfort plays an important role in determining the public acceptance of autonomous
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning
Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing
methods to address these problems are time-consuming and not adaptive to changing …
methods to address these problems are time-consuming and not adaptive to changing …