Heuristics‐oriented overtaking decision making for autonomous vehicles using reinforcement learning
This study presents a three‐lane highway overtaking strategy for an automated vehicle,
which is based on a heuristic planning reinforcement learning algorithm. The proposed …
which is based on a heuristic planning reinforcement learning algorithm. The proposed …
Addressing inherent uncertainty: Risk-sensitive behavior generation for automated driving using distributional reinforcement learning
J Bernhard, S Pollok, A Knoll - 2019 IEEE Intelligent Vehicles …, 2019 - ieeexplore.ieee.org
For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …
its potential to solve complex classification and control problems. However, existing RL …
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 …
Improved deep reinforcement learning with expert demonstrations for urban autonomous driving
Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …
Trustworthy safety improvement for autonomous driving using reinforcement learning
Reinforcement learning (RL) can learn from past failures and has the potential to provide
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
Autonomous highway driving using deep reinforcement learning
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Autonomous vehicle navigation using evolutionary reinforcement learning
A Stafylopatis, K Blekas - European Journal of Operational Research, 1998 - Elsevier
Reinforcement learning schemes perform direct on-line search in control space. This makes
them appropriate for modifying control rules to obtain improvements in the performance of a …
them appropriate for modifying control rules to obtain improvements in the performance of a …
Adaptive behaviour selection for autonomous vehicle through naturalistic speed planning
M Rodrigues, G Gest, A McGordon… - 2017 IEEE 20th …, 2017 - ieeexplore.ieee.org
As autonomous technologies in ground vehicle application begin to mature, there is a
greater acceptance that they can eventually exhaust human involvement in the driving …
greater acceptance that they can eventually exhaust human involvement in the driving …
Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion
Accurate behavior anticipation is essential for autonomous vehicles when navigating in
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …