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Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
[article]
2020
arXiv
pre-print
Preserved Fulltext
Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. ...
This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous ...
In contrast to the related work, this paper investigates an RL method for tactical decision-making in autonomous driving that can estimate the uncertainty of its decision, based arXiv:2004.10439v1 [cs.RO ...
arXiv:2004.10439v1
fatcat:xlqp2jupvrg4lnwjzp5phzedza
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections
[article]
2020
arXiv
pre-print
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This paper investigates how a Bayesian reinforcement learning method can be used to create a tactical decision-making agent for autonomous driving in an intersection scenario, where the agent can estimate ...
The coefficient of variation in the estimated Q-values of the ensemble members is used to approximate the uncertainty, and a criterion that determines if the agent is sufficiently confident to make a particular ...
McAllister et al. further discuss the importance of estimating the uncertainty of decisions in autonomous driving [11] . ...
arXiv:2006.09786v1
fatcat:g5krdbp335gvja25gd4y7vaxga
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
[article]
2019
arXiv
pre-print
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Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety ...
In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain ...
Deep RL is efficient in learning arbitrary policies defining specific goals. In [8] , a tactical decision making for lane changing in highway driving scenarios is being performed using deep RL. ...
arXiv:1909.11538v1
fatcat:frgmz75pu5ah7ogqcbkt2kwuee
Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook
[article]
2021
arXiv
pre-print
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Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. ...
In addition, applications of decision-making methods in existing autonomous vehicles are summarized. ...
review mainly on learning-based decision-making methods that have emerged in recent years with the summarization of applications in existing autonomous vehicles. ...
arXiv:2107.01110v1
fatcat:ohffatmrmfbzdlihgvywryzupa
A Decision-Making Model for Autonomous Vehicles at Urban Intersections Based on Conflict Resolution
2021
Journal of Advanced Transportation
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The reliability and effectiveness of the tactical decision-making model was verified by simulations. ...
The decision-making models that are able to deal with complex and dynamic urban intersections are critical for the development of autonomous vehicles. ...
To address this problem, this paper will focus on developing a tactical decisionmaking model for autonomous vehicles in intersection crossing scenarios. e problems of robust tactical decision-making for ...
doi:10.1155/2021/8894563
fatcat:vkvrhgljlrezte256fevb5wfka
Hierarchical Decision Making
[chapter]
2009
Handbook of Learning and Approximate Dynamic Programming
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Decision making must be made within an appropriate context; we contend that such context is best represented by a hierarchy of states. ...
Once learned, the resulting mathematical models may be combined with the techniques of reinforcement learning to predict behavior and anticipate the needs of the user, delivering appropriate data, visualizations ...
Moving Forward Although Reinforcement Learning methods have been successfully integrated with probabilistic graphical networks, which allow us to build autonomous decision making systems that learn and ...
doi:10.1109/9780470544785.ch8
fatcat:kffb3eyh5bbgnc7otfnflgv5me
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
[article]
2019
arXiv
pre-print
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Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. ...
This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. ...
TACTICAL DECISION MAKING FRAMEWORK This paper introduces a framework that combines planning and learning for tactical decision making in the autonomous driving domain. ...
arXiv:1905.02680v1
fatcat:rbfhjw3k7jd73kpgcdw2uv7lbi
Front Matter: Volume 11021
2019
Unmanned Systems Technology XXI
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These two-number sets start with 00, 01, 02, 03, 04, ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
to decision making under uncertainty [11021-30] 11021 0T Stochastic functional laws of the iterated logarithm with applications to learning and control [11021-31] Proc. of SPIE Vol. 11021 1102101-4 [11021 ...
doi:10.1117/12.2536312
fatcat:2beoa75kr5dvjb7qzhhxhilrim
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
[article]
2020
arXiv
pre-print
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Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission. ...
This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. ...
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon Fig. 1. ...
arXiv:2008.11852v1
fatcat:ozyiffczh5fyzbh3s6d3cq5vje
Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning
2019
Electronics
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Several simulations are performed to train the algorithm in two scenarios: navigating a roundabout with and without surrounding traffic. ...
Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. ...
For example, in [11, 26, 27] various RL methods are proposed in which autonomous vehicles learn to make decisions while interacting with simulated traffic. ...
doi:10.3390/electronics8121536
fatcat:a7dod2evqbddnlvazefknzqrry
Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways
[article]
2020
arXiv
pre-print
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Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. ...
It is shown that the decision model can replicate human drivers' discretionary lane-change decisions with up to 92% accuracy. ...
Recently, deep reinforcement learning has been proposed for tactical decision-making [4] . ...
arXiv:2007.14032v2
fatcat:53idpayfxrgttnbxgp7djcrbqm
Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics
2021
Machine Learning and Knowledge Extraction
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Reinforcement Learning (RL) is an approach to simulate the human's natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. ...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems. ...
This makes DL unpractical in many domains. Reinforcement Learning imitates the learning process of humans. It is trained by making and then avoiding mistakes. ...
doi:10.3390/make3040043
doaj:45bf00de595c44d186fa3d200589c1c5
fatcat:qx4srh7qabgjvd5l6lj6nulhxa
Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles
[article]
2021
arXiv
pre-print
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For autonomous vehicles integrating onto roadways with human traffic participants, it requires understanding and adapting to the participants' intention and driving styles by responding in predictable ...
This paper proposes a reinforcement learning based negotiation-aware motion planning framework, which adopts RL to adjust the driving style of the planner by dynamically modifying the prediction horizon ...
For tactical decision making, Hoel et al. introduced a general framework, which combines planning and learning in the form of Monte Carlo tree search and RL [27] . ...
arXiv:2107.03600v1
fatcat:tebaof5anzcd7dv6jp7eugznqu
Abstracts
2020
IEEE Transactions on Intelligent Vehicles
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We propose a new method that makes full use of the advantages of traditional methods and deep learning methods via incorporating human domain knowledge into the neural network model to reduce the demand ...
Quantitative experiments show that the pretrained model achieves better performance than random initialization in almost all cases; furthermore, our method can achieve similar performance with fewer manual ...
Tactical Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users ...
doi:10.1109/tiv.2020.2973018
fatcat:qgsawvfx6rgfpgknnwdmfixrme
Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability
2020
Sustainability
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By referring to studies on uncertainty in decision making, this research describes three dimensions of uncertainty, namely informational, environmental and intentional. ...
To understand how to manage uncertainty in AI-enabled decision-making applications, the authors conduct a literature review using content analysis with practical approaches. ...
As an example, the autonomous vehicle is a novel AI application that provides humans with convenient transport services by autonomously analyzing road conditions and making driving decisions. ...
doi:10.3390/su12218758
fatcat:54jbxassl5cohfjn4pmsfvjtyq
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