An actor-critic based learning method for decision-making and planning of autonomous vehicles

2021 ◽  
Author(s):  
Can Xu ◽  
WanZhong Zhao ◽  
QingYun Chen ◽  
ChunYan Wang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Learning Method

Research on decision-making of autonomous vehicle following based on reinforcement learning method

2019 ◽  
Vol 46 (3) ◽  
pp. 444-452 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Kelong Wang ◽  
Guotao Xie ◽  
Yuchao Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning System ◽  
Explicit Calculation ◽  
Gradient Descent Method ◽  
Learning Method ◽  
Content Type ◽  
Car Following

Purpose Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

Multi-Cameras based Decision Making at Mini-Roundabouts for Autonomous Vehicles

2019 ◽  
Author(s):  
Weichao Wang ◽  
Quang A Nguyen ◽  
Paul Wai Hing Chung ◽  
Qinggang Meng
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals Heuristics-oriented overtaking decision making for autonomous vehicles using reinforcement learning

2020 ◽  
Vol 10 (4) ◽  
pp. 417-424
Author(s):  
Teng Liu ◽  
Bing Huang ◽  
Zejian Deng ◽  
Hong Wang ◽  
Xiaolin Tang ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles

scholarly journals Mobile Game Education About using Capitals Hiragana and Katakana Fisher-Yates Shuffle Algorithm and Fuzzy Tsukamoto

2021 ◽  
Vol 5 (9) ◽  
pp. 30-34
Author(s):  
Rahmiati* . ◽  
Rika Melyanti ◽  
Suryani Des ◽  
Ambiyar .
Keyword(s):  
Decision Making ◽  
Mobile Devices ◽  
Good Accuracy ◽  
Educational Games ◽  
Vocational School ◽  
Educational Game ◽  
Learning Method ◽  
Mobile Game ◽  
New Learning

Japanese is a different language because it uses the letters Katakana and Hiragana. Japanese learning at the Kansai Vocational School Pekanbaru encountered several obstacles including the lack of learning facilities and a learning atmosphere that tends to be boring so that many students have difficulty learning and choosing to play games. Educational games on mobile devices are a new learning method that is considered to be more attractive to someone to learn. Fisher-Yates is a randomization technique on questions so questions that come out will be different and can be generated without repetition and duplication. Fuzzy Tsukamoto is a method used in decision-making to determine the score at the end of the quiz. From this research, it can be found that Fisher-Yates can determine the randomization solution that is not multiple and varied object randomization. Fuzzy Tsukamoto has a fairly good accuracy between calculations based on the system and calculations manually, although it does not show results that are 100% the same in each calculation. Educational game recognizing the letters Katakana and Hiragana is expected to help students overcome difficulties in understanding and learning Japanese related to the mastery of the letters Katakana and Hiragana.

scholarly journals Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles*

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 9413-9420 ◽  
Author(s):  
Jianqiang Nie ◽  
Jian Zhang ◽  
Wanting Ding ◽  
Xia Wan ◽  
Xiaoxuan Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Lane Changing
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