An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning
Y Liu, S Diao - PLoS one, 2024 - journals.plos.org
As autonomous driving technology continues to advance and gradually become a reality,
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning
(PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning …
(PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning …
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
Recent advancements in motion planning for Autonomous Vehicles (AVs) show great
promise in using expert driver behaviors in non-stationary driving environments. However …
promise in using expert driver behaviors in non-stationary driving environments. However …
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 …
Behavior and interaction-aware motion planning for autonomous driving vehicles based on hierarchical intention and motion prediction
Safe motion planning in complex and interactive environments is one of the major
challenges for developing autonomous vehicles. In this paper, we propose an interaction …
challenges for developing autonomous vehicles. In this paper, we propose an interaction …
Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios
B Brito, A Agarwal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
An end-to-end deep reinforcement learning approach for the long-term short-term planning on the frenet space
Tactical decision making and strategic motion planning for autonomous highway driving are
challenging due to the complication of predicting other road users' behaviors, diversity of …
challenging due to the complication of predicting other road users' behaviors, diversity of …
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 …
Behavior modeling and motion planning for autonomous driving using artificial intelligence
M Zhu - 2022 - digital.lib.washington.edu
With an emphasis on longitudinal driving, this dissertation aims to develop data-driven
models that improve existing driving behavior models and facilitate various kinds of …
models that improve existing driving behavior models and facilitate various kinds of …
Interaction-aware planning with deep inverse reinforcement learning for human-like autonomous driving in merge scenarios
J Nan, W Deng, R Zhang, Y Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …
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- traffic scenarios motion planning
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