Deep reinforcement learning with enhanced safety for autonomous highway driving
In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …
The proposed framework leverages merits of both rule-based and learning-based …
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 …
An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle
W Wang, T Qie, C Yang, W Liu… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
In the future complex intelligent transportation environments, lane-changing behavior of
surrounding vehicles is a significant factor affecting the driving safety. It is necessary to …
surrounding vehicles is a significant factor affecting the driving safety. It is necessary to …
Deep reinforcement learning for personalized driving recommendations to mitigate aggressiveness and riskiness: Modeling and impact assessment
EG Mantouka, EI Vlahogianni - Transportation research part C: emerging …, 2022 - Elsevier
Most driving recommendation and assistance systems, such as Advanced Driving
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Deep distributional reinforcement learning based high-level driving policy determination
Even though some of the driver assistant systems have been commercialized to provide
safety and convenience to the driver, they can be applied for autonomous driving in limited …
safety and convenience to the driver, they can be applied for autonomous driving in limited …
Hierarchical reinforcement learning for autonomous decision making and motion planning of intelligent vehicles
Y Lu, X Xu, X Zhang, L Qian, X Zhou - IEEE Access, 2020 - ieeexplore.ieee.org
Autonomous decision making and motion planning in complex dynamic traffic environments,
such as left-turn without traffic signals and multi-lane merging from side-ways, are still …
such as left-turn without traffic signals and multi-lane merging from side-ways, are still …
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 …
Highway decision-making and motion planning for autonomous driving via soft actor-critic
In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
is constructed in the highway driving scenario based on deep reinforcement learning. In the …