Planning and decision-making for autonomous vehicles
W Schwarting, J Alonso-Mora… - Annual Review of Control …, 2018 - annualreviews.org
In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …
Variable speed limit and ramp metering for mixed traffic flows: A review and open questions
The trend of increasing traffic demand is causing congestion on existing urban roads,
including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and …
including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
Cooperation-aware reinforcement learning for merging in dense traffic
Decision making in dense traffic can be challenging for autonomous vehicles. An
autonomous system only relying on predefined road priorities and considering other drivers …
autonomous system only relying on predefined road priorities and considering other drivers …
Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …
Decision-making technology for autonomous vehicles: Learning-based methods, applications and future outlook
Q Liu, X Li, S Yuan, Z Li - 2021 IEEE International Intelligent …, 2021 - ieeexplore.ieee.org
Autonomous vehicles have a great potential in the application of both civil and military fields,
and have become the focus of research with the rapid development of science and …
and have become the focus of research with the rapid development of science and …
Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
Novel decision-making strategy for connected and autonomous vehicles in highway on-ramp merging
Z el abidine Kherroubi, S Aknine… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High-speed highway on-ramp merging is a significant challenge toward realizing fully
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
Spatial-temporal ConvLSTM for vehicle driving intention prediction
Driving intention prediction from a bird's-eye view has always been an active research area.
However, existing research, on one hand, has only focused on predicting lane change …
However, existing research, on one hand, has only focused on predicting lane change …
Causal-based time series domain generalization for vehicle intention prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …