Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments
S Chen, X Hu, J Zhao, R Wang, M Qiao - World Electric Vehicle Journal, 2024 - mdpi.com
Decision-making and planning are the core aspects of autonomous driving systems. These
factors are crucial for improving the safety, driving experience, and travel efficiency of …
factors are crucial for improving the safety, driving experience, and travel efficiency of …
A deep reinforcement learning driving policy for autonomous road vehicles
This work regards our preliminary investigation on the problem of path planning for
autonomous vehicles that move on a freeway. We approach this problem by proposing a …
autonomous vehicles that move on a freeway. We approach this problem by proposing a …
Context‐aware trajectory prediction for autonomous driving in heterogeneous environments
The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …
remains a challenging task for autonomous driving due to several critical issues, such as …
Interactive planning for autonomous driving in intersection scenarios without traffic signs
Efficient intersection planning is one of the most challenging tasks for an autonomous
vehicle at present. Politeness to other traffic participants and reaction to surrounding …
vehicle at present. Politeness to other traffic participants and reaction to surrounding …
[HTML][HTML] Optimizing trajectories for highway driving with offline reinforcement learning
B Mirchevska, M Werling, J Boedecker - Frontiers in Future …, 2023 - frontiersin.org
Achieving feasible, smooth and efficient trajectories for autonomous vehicles which
appropriately take into account the long-term future while planning, has been a long …
appropriately take into account the long-term future while planning, has been a long …
Integrating intuitive driver models in autonomous planning for interactive maneuvers
K Driggs-Campbell, V Govindarajan… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …
vehicles being released on the road in the near future. However, it can be assumed that this …
iplan: Intent-aware planning in heterogeneous traffic via distributed multi-agent reinforcement learning
Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …
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 …
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- reinforcement learning autonomous vehicles
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