Decision making for highway complex scenario by improved safety field with learning process

2021 ◽  
pp. 095440702110532
Author(s):  
Can Xu ◽  
Wanzhong Zhao ◽  
Jingqiang Liu ◽  
Feng Chen
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Action Sequence ◽  
Decision Algorithm ◽  
The Road

To improve the agility and efficiency of the highway decision-making system and overcome the local optimal dilemma of the existing safety field, this paper builds an improved safety field to reflect the advantage of the reachable states and the learning process is further employed to make the decision long-term optimal. Firstly, the improved safety field is prepared by the kinematic model-based prediction of surrounding vehicles and the boundary is determined elaborately to ensure real-time performance. Then, the field is constructed by three individual fields. One is the kinematic field, which is built based the safe-distance model to measure the colliding risk of both moving or no-moving objects accurately. Another is the road field that reflects the lane-marker constraint. The last is the efficiency field, which is introduced creatively to improve efficiency. Furthermore, the learning algorithm is adopted to learn the long-term optimal state-action sequence in the safety field. Finally, the simulations are conducted in Prescan platform to validate the feasibility of the improved safety field in complex scenarios. The results show that the proposed decision algorithm can always drive autonomous vehicle to the state with a long-term optimal payoff and can improve the overall performance compared to the existing pure safety field and the interaction-aware method.

scholarly journals Coronary artery decision algorithm trained by two-step machine learning algorithm

RSC Advances ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 4014-4022
Author(s):  
Young Woo Kim ◽  
Hee-Jin Yu ◽  
Jung-Sun Kim ◽  
Jinyong Ha ◽  
Jongeun Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Decision Making ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Flow Characteristics ◽  
Machine Learning Algorithm ◽  
Decision Algorithm ◽  
Computational Fluid Dynamics Cfd ◽  
Biometric Features

A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).

Trust Learning and Estimation

2017 ◽  
pp. 317-330
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Learning Process ◽  
Learning Algorithm ◽  
Estimation Algorithm ◽  
Decision Making Process ◽  
Self Organizing Map ◽  
The Core ◽  
Self Organizing

Predict uncertainty is critic in decision making process, especially for the complex systems. This chapter aims to discuss the theory involved in Self-Organizing Map (SOM) and its learning process, SOM based Trust Learning Algorithm (STL), SOM based Trust Estimation Algorithm (STL) as well as features of generated trust patterns. Several patterns are discussed within context. Both algorithms and how they are processed have been described in detail. It is found that SOM based Trust Estimation algorithm is the core algorithm that help agent make trustworthy or untrustworthy decisions.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change

2021 ◽  
pp. 095440702110397
Author(s):  
Yunfeng Huang ◽  
Wanzhong Zhao ◽  
Can Xu ◽  
Songchun Zou ◽  
Han Zhang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Risk Function ◽  
Interacting Multiple Model ◽  
Lane Change ◽  
Multiple Model ◽  
Collision Risk ◽  
Lane Changing ◽  
Decision Algorithm ◽  
Risk Environments

In order to make safe and reasonable decisions in some high-risk environments such as the mandatory lane change, we propose an IMM-based partially observable Markov decision process (POMDP) decision algorithm using the collision-risk function which combines the time-to-collision (TTC), the intervehicular time (IT), and the collision function for mandatory lane change. The newly proposed collision-risk function contains two parts: the vehicle impact factor and the collision function, which is used to assess the risk and determines whether the autonomous vehicle collides with surrounding vehicles. The IMM-base POMDP is used for decision-making and we apply the Monte Carlo Tree Search (MCTS) to solve the problem. In the decision-making process, the belief state is obtained by the Interacting Multiple Model (IMM) algorithm. With the collision-risk function and the probability distribution of the states of surrounding vehicles in the future, the proposed POMDP decision algorithm can determine whether the autonomous vehicle accelerates lane changing or decelerates lane changing, and obtain the acceleration corresponding to each path point. Finally, in order to verify the effectiveness of the algorithm, we perform a driver-in-the-loop simulation through Prescan. We use aggressive driver and conservative driver to control the rear vehicle of the target lane, respectively. Simulation results show that the proposed algorithm can accurately predict the accelerations of surrounding vehicles and make safe and reasonable decisions under two scenarios, which is superior to the general POMDP.

scholarly journals Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881716 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Guotao Xie ◽  
Bo Cheng
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Sequential Decision ◽  
Inverse Reinforcement Learning ◽  
Car Following ◽  
Reward Function ◽  
Automatic Driving

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment.

scholarly journals Collective preference learning in the best-of-n problem

2021 ◽  
Author(s):  
Michael Crosscombe ◽  
Jonathan Lawry
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Learning Algorithm ◽  
Autonomous Systems ◽  
Distributed Learning ◽  
Agent Based ◽  
Decision Making Problem ◽  
Local Feedback ◽  
Shared Objective ◽  
Collective Preference

AbstractDecentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information about all n alternatives while then systematically discarding information about all but the best option. However, for applications such as search and rescue in which learning the ranking of options is useful or crucial, best-of-n decision-making can be wasteful and costly. Instead, we investigate a more general distributed learning process in which agents learn a preference ordering over all of the n options. More specifically, we introduce a distributed rank learning algorithm based on three-valued logic. We then use agent-based simulation experiments to demonstrate the effectiveness of this model. In this context, we show that a population of agents are able to learn a total ordering over the n options and furthermore the learning process is robust to evidential noise. To demonstrate the practicality of our model, we restrict the communication bandwidth between the agents and show that this model is also robust to limited communications whilst outperforming a comparable probabilistic model under the same communication conditions.

Issues of Capacity and Decision-Making in the Context of Delirium

2015 ◽  
Vol 24 (4) ◽  
pp. 140-145
Author(s):  
Kevin R. Patterson
Keyword(s):  
Decision Making ◽  
Patient Autonomy ◽  
Care Setting ◽  
Cognitive Change ◽  
Treatment Team ◽  
Precipitating Factors ◽  
Term Care ◽  
Decision Making Capacity ◽  
Fundamental Consideration

Decision-making capacity is a fundamental consideration in working with patients in a clinical setting. One of the most common conditions affecting decision-making capacity in patients in the inpatient or long-term care setting is a form of acute, transient cognitive change known as delirium. A thorough understanding of delirium — how it can present, its predisposing and precipitating factors, and how it can be managed — will improve a speech-language pathologist's (SLPs) ability to make treatment recommendations, and to advise the treatment team on issues related to communication and patient autonomy.

Perancangan aplikasi multi criteria decision making dalam penerimaan beasiswa kepada dosen studi lanjut STMIK Balikpapan menggunakan metode SAW

2018 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Muslimin B ◽  
Sumardi Sumardi
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Teaching And Learning ◽  
Teaching Assistants ◽  
Multi Criteria Decision Making ◽  
Scholarship Recipients ◽  
The Matrix ◽  
Application Decision ◽  
High Degree ◽  
New Student

 Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan.

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