Learning-based safety-critical motion planning with input-to-state barrier certificate
Motion planning in an effective and safe manner is a critical yet challenging task for
autonomous driving. Learning-based framework as a new fashion in simulation and …
autonomous driving. Learning-based framework as a new fashion in simulation and …
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 …
Reinforcement learning based trajectory planning for autonomous vehicles
Z Wang, J Tu, C Chen - 2021 China Automation Congress …, 2021 - ieeexplore.ieee.org
The trajectory planning of autonomous vehicles requires making safe sequential decisions
instantaneously. The most significant challenge is the uncertainties brought by complex …
instantaneously. The most significant challenge is the uncertainties brought by complex …
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
This study introduces a novel approach to autonomous motion planning, informing an
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
Active safe motion planning for intelligent vehicles in dynamic environments
H Tian, J Wang, H Huang - 2021 5th CAA International …, 2021 - ieeexplore.ieee.org
Motion planning is an essential component in intelligent vehicle study. Rapidly-exploring
Random Tree (RRT) and its variants are popular algorithms that have been successfully …
Random Tree (RRT) and its variants are popular algorithms that have been successfully …
An optimization-based motion planning method for autonomous driving vehicle
S Luo, X Li, Z Sun - 2020 3rd international conference on …, 2020 - ieeexplore.ieee.org
With the progress of autonomous driving technology, obtaining a safe and smooth trajectory
in complex environments is the focus of motion planning in recent years. This paper …
in complex environments is the focus of motion planning in recent years. This paper …
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 …
TDR-OBCA: A reliable planner for autonomous driving in free-space environment
This paper presents an optimization-based collision avoidance trajectory generation method
for autonomous driving in free-space environments, with enhanced robustness, driving …
for autonomous driving in free-space environments, with enhanced robustness, driving …
Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
Model predictive control (MPC) is widely used for motion planning, particularly in
autonomous driving. Real-time capability of the planner requires utilizing convex …
autonomous driving. Real-time capability of the planner requires utilizing convex …