Generating efficient behaviour with predictive visibility risk for scenarios with occlusions
Safety is one of the most significant challenges for autonomous driving. However,
autonomous vehicles need also to be as efficient as possible while ensuring safety. In this …
autonomous vehicles need also to be as efficient as possible while ensuring safety. In this …
Learn collision-free self-driving skills at urban intersections with model-based reinforcement learning
Intersection is one of the most complex and accident-prone urban traffic scenarios for
autonomous driving wherein making safe and computationally efficient decisions with high …
autonomous driving wherein making safe and computationally efficient decisions with high …
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 …
Navigating Unsignalized Intersections: A Predictive Approach for Safe and Cautious Autonomous Driving
Collision avoidance at unsignalized intersections is critical to autonomous vehicle
technology. Our work addresses the challenging problem of online speed planning along a …
technology. Our work addresses the challenging problem of online speed planning along a …
A review of end-to-end autonomous driving in urban environments
D Coelho, M Oliveira - IEEE Access, 2022 - ieeexplore.ieee.org
Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …
with complex and unpredictable scenarios. Traditional modular approaches focus on …
A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning
Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
Decision-making for oncoming traffic overtaking scenario using double DQN
S Mo, X Pei, Z Chen - 2019 3rd Conference on Vehicle Control …, 2019 - ieeexplore.ieee.org
Great progress has been made in the field of machine learning in recent years. And learning-
based methods have been widely utilized for developing highly autonomous vehicle. To this …
based methods have been widely utilized for developing highly autonomous vehicle. To this …
Safe and rule-aware deep reinforcement learning for autonomous driving at intersections
C Zhang, K Kacem, G Hinz… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
Visual-based autonomous driving deployment from a stochastic and uncertainty-aware perspective
End-to-end visual-based imitation learning has been widely applied in autonomous driving.
When deploying the trained visual-based driving policy, a deterministic command is usually …
When deploying the trained visual-based driving policy, a deterministic command is usually …
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 …