Nothing Special   »   [go: up one dir, main page]

Yang et al., 2022 - Google Patents

Decision-making in autonomous driving by reinforcement learning combined with planning & control

Yang et al., 2022

Document ID
7826834946832159368
Author
Yang Z
Pei X
Xu J
Zhang X
Xi W
Publication year
Publication venue
2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI)

External Links

Snippet

In recent years, there are more and more studies on reinforcement learning (RL) and RL algorithms have achieved good performance in autonomous driving simulation experiments. But the simulation experiments are too idealistic, their safety cannot be guaranteed in …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Similar Documents

Publication Publication Date Title
CN110969848B (en) Automatic driving overtaking decision method based on reinforcement learning under opposite double lanes
CN110745136B (en) Driving self-adaptive control method
Naveed et al. Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
CN114013443B (en) Automatic driving vehicle lane change decision control method based on hierarchical reinforcement learning
Wang et al. High-level decision making for automated highway driving via behavior cloning
Yan et al. A multi-vehicle game-theoretic framework for decision making and planning of autonomous vehicles in mixed traffic
Qiao et al. Behavior planning at urban intersections through hierarchical reinforcement learning
Deshpande et al. Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
CN113581182A (en) Method and system for planning track change of automatic driving vehicle based on reinforcement learning
Crosato et al. Human-centric autonomous driving in an av-pedestrian interactive environment using svo
Lodhi et al. Autonomous vehicular overtaking maneuver: A survey and taxonomy
Sun et al. Human-like highway trajectory modeling based on inverse reinforcement learning
Chen et al. Automatic overtaking on two-way roads with vehicle interactions based on proximal policy optimization
Yang et al. Decision-making in autonomous driving by reinforcement learning combined with planning & control
Shi et al. Efficient Lane-changing Behavior Planning via Reinforcement Learning with Imitation Learning Initialization
Zhang et al. Safe and rule-aware deep reinforcement learning for autonomous driving at intersections
Wang et al. A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level
CN113033902B (en) Automatic driving lane change track planning method based on improved deep learning
Nan et al. Interaction-aware planning with deep inverse reinforcement learning for human-like autonomous driving in merge scenarios
CN110390398B (en) Online learning method
CN112835362B (en) Automatic lane change planning method and device, electronic equipment and storage medium
Zhao et al. Imitation of real lane-change decisions using reinforcement learning
Tang et al. Research on decision-making of lane-changing of automated vehicles in highway confluence area based on deep reinforcement learning
Yang et al. Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction
Zeng et al. Risk-aware deep reinforcement learning for decision-making and planning of autonomous vehicles