Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
… driving data and combining driving features related to safety, efficiency, and comfort. With
the developed reward function, the RL agent learns to control vehicle … for safety checks. The …
the developed reward function, the RL agent learns to control vehicle … for safety checks. The …
Dense reinforcement learning for safety validation of autonomous vehicles
… for traditional deep-reinforcement-learning … automated vehicle in both highway and urban
test tracks with an augmented-reality environment, combining simulated background vehicles …
test tracks with an augmented-reality environment, combining simulated background vehicles …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
… on Deep Reinforcement Learning (DRL) approach. DRL combines the classic reinforcement
learning with deep … Peng, “Safe reinforcement learning for autonomous vehicles through …
learning with deep … Peng, “Safe reinforcement learning for autonomous vehicles through …
Combining planning and deep reinforcement learning in tactical decision making for autonomous driving
… One of the technical challenges for autonomous driving is to be able to make safe and … ,
based on uncertain sensor information, while interacting with other traffic participants. A decision …
based on uncertain sensor information, while interacting with other traffic participants. A decision …
Deep reinforcement learning for autonomous driving: A survey
… for desires to enable comfort driving and trajectory planning. The deep reinforcement
learning algorithms for control such as DDPG and safety based control are combined in [141], …
learning algorithms for control such as DDPG and safety based control are combined in [141], …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
… human drivers and autonomous vehicles characterized? We … and machine learning, to join
forces towards creating a safe … scenarios such as lane-keeping, lane-change, merging, and …
forces towards creating a safe … scenarios such as lane-keeping, lane-change, merging, and …
A survey of deep learning techniques for autonomous driving
… , as well as the deep reinforcement learning paradigm. These … for autonomous driving, such
as their safety, training data sources, … of learning controllers is that they optimally combine …
as their safety, training data sources, … of learning controllers is that they optimally combine …
A survey of deep rl and il for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
… robotics and video games, the use of deep reinforcement learning (DRL) and deep imitation
… It is found that driving safety has been well studied. A typical strategy is to combine with …
… It is found that driving safety has been well studied. A typical strategy is to combine with …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
… driving safety, we proposed a lane change decision-making framework based on deep
reinforcement learning … In this paper, deep reinforcement learning algorithms combining with risk …
reinforcement learning … In this paper, deep reinforcement learning algorithms combining with risk …
Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: A reinforcement learning approach
… a specific model design corresponding to a typical circumstance such as highway-merging. …
We extract the first 45 vehicles to compare the fuel consumption and safety risk under the …
We extract the first 45 vehicles to compare the fuel consumption and safety risk under the …