Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
Integrating deep reinforcement learning with optimal trajectory planner for automated driving
Trajectory planning in the intersection is a challenging problem due to the strong uncertain
intentions of surrounding agents. Conventional methods may fail in some corner cases …
intentions of surrounding agents. Conventional methods may fail in some corner cases …
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 …
Vision-based trajectory planning via imitation learning for autonomous vehicles
Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …
a critical capability for autonomous driving. To this end, we develop a vision and imitation …
A deep reinforcement learning approach for long-term short-term planning on frenet frame
Tactical decision-making and strategic motion planning for autonomous highway driving are
challenging due to predicting other road users' behaviors, diversity of environments, and …
challenging due to predicting other road users' behaviors, diversity of environments, and …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …
DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
Safe reinforcement learning with policy-guided planning for autonomous driving
J Rong, N Luan - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
The uncertainty and complexity of autonomous driving make Deep Reinforcement Learning
(DRL) appealing. DRL can optimize the expected reward by interacting with environments …
(DRL) appealing. DRL can optimize the expected reward by interacting with environments …
A reinforcement learning benchmark for autonomous driving in intersection scenarios
In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …