KB-Tree: Learnable and Continuous Monte-Carlo Tree Search for Autonomous Driving Planning

Lei, Lanxin; Luo, Ruiming; Zheng, Renjie; Wang, Jingke; Zhang, Jianwei; Qiu, Cong; Ma, Liulong; Jin, Liyang; Zhang, Ping; Chen, Junbo

doi:10.1109/iros51168.2021.9636442

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…Notably, NVIDIA [7] employed a largescale convolutional neural network (CNN) to train the models, achieving successful navigation of commercial vehicles across a diverse range of driving conditions encompassing highways and small residential roads. Furthermore, Lanxin Lei et al proposed the KB-Tree method [8], which leverages kernel regression, Bayesian optimization, and graph neural network (GNN) techniques to achieve notable overall performance.…”

Section: Introductionmentioning

confidence: 99%

Efficiency study of VGG networks in autonomous driving tasks

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomousdriving tasks. The objective is to demonstrate the effectiveness of employing this neural network to solve the problemand to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without theinclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to addressdiverse problems. Image recognition holds particular significance among these challenges. The practical problemunderinvestigation in this study pertains to the identification of roads with varying curvatures and their correspondence tothevehicle’s steering angles for the purpose of autonomous vehicle control. This paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solvingtheautonomous driving problems and a performance comparison of these methods. The experimental results indicatethat VGG 19 achieves the highest accuracy of all neural networks.

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Section: Introductionmentioning

confidence: 99%

Efficiency study of VGG networks in autonomous driving tasks

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Data Aggregation (DAgger) Algorithm Using Adversarial Agent Policy for Dynamic Situations

Ahn,

Shin,

Koo

et al. 2024

Lecture Notes in Networks and Systems

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Beyond games: a systematic review of neural Monte Carlo tree search applications

Kemmerling,

Lütticke,

Schmitt

2023

Appl Intell

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The advent of AlphaGo and its successors marked the beginning of a new paradigm in playing games using artificial intelligence. This was achieved by combining Monte Carlo tree search, a planning procedure, and deep learning. While the impact on the domain of games has been undeniable, it is less clear how useful similar approaches are in applications beyond games and how they need to be adapted from the original methodology. We perform a systematic literature review of peer-reviewed articles detailing the application of neural Monte Carlo tree search methods in domains other than games. Our goal is to systematically assess how such methods are structured in practice and if their success can be extended to other domains. We find applications in a variety of domains, many distinct ways of guiding the tree search using learned policy and value functions, and various training methods. Our review maps the current landscape of algorithms in the family of neural monte carlo tree search as they are applied to practical problems, which is a first step towards a more principled way of designing such algorithms for specific problems and their requirements.

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KB-Tree: Learnable and Continuous Monte-Carlo Tree Search for Autonomous Driving Planning

Cited by 6 publications

References 18 publications

Efficiency study of VGG networks in autonomous driving tasks

Efficiency study of VGG networks in autonomous driving tasks

Data Aggregation (DAgger) Algorithm Using Adversarial Agent Policy for Dynamic Situations

Beyond games: a systematic review of neural Monte Carlo tree search applications

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