Incorporating multi-context into the traversability map for urban autonomous driving using deep inverse reinforcement learning
C Jung, DH Shim - IEEE Robotics and Automation Letters, 2021 - ieeexplore.ieee.org
Autonomous driving in an urban environment with surrounding agents remains challenging.
One of the key challenges is to accurately predict the traversability map that probabilistically …
One of the key challenges is to accurately predict the traversability map that probabilistically …
Autonomous highway driving using deep reinforcement learning
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
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 …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Safe planning for self-driving via adaptive constrained ILQR
Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR, has been
recently proposed for motion planning problems of autonomous vehicles to deal with …
recently proposed for motion planning problems of autonomous vehicles to deal with …
Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …
urban autonomous driving where there are complex contextual factors, such as lane …
A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree
C Zhao, Y Zhu, Y Du, F Liao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Trajectory planning is essential for self-driving vehicles and has stringent requirements for
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving
Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
Experience-based heuristic search: Robust motion planning with deep Q-learning
Interaction-aware planning for autonomous driving requires an exploration of a
combinatorial solution space when using conventional search-or optimization-based motion …
combinatorial solution space when using conventional search-or optimization-based motion …
A hierarchical architecture for sequential decision-making in autonomous driving using deep reinforcement learning
M Moghadam, GH Elkaim - arXiv preprint arXiv:1906.08464, 2019 - arxiv.org
Tactical decision making is a critical feature for advanced driving systems, that incorporates
several challenges such as complexity of the uncertain environment and reliability of the …
several challenges such as complexity of the uncertain environment and reliability of the …