Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature

• URL: http://arxiv.org/abs/2207.13181v2
• Date: Tue, 17 Oct 2023 21:02:54 GMT
• Title: Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
• Authors: Kevin Osanlou, Christophe Guettier, Tristan Cazenave, Eric Jacopin
• Abstract summary: This short review aims to make the reader familiar with state-of-the-art works relating to planning, scheduling and learning. First, we study state-of-the-art planning algorithms. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs.
• Score: 5.015229992316947
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This short review aims to make the reader familiar with state-of-the-art works relating to planning, scheduling and learning. First, we study state-of-the-art planning algorithms. We give a brief introduction of neural networks. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs. We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date. Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• Simple and Effective Transfer Learning for Neuro-Symbolic Integration [50.592338727912946]
  A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning. Most of these methods exploit a neural network to map perceptions to symbols and a logical reasoner to predict the output of the downstream task. They suffer from several issues, including slow convergence, learning difficulties with complex perception tasks, and convergence to local minima. This paper proposes a simple yet effective method to ameliorate these problems.
  arXiv  Detail & Related papers  (2024-02-21T15:51:01Z)
• Reasoning Algorithmically in Graph Neural Networks [1.8130068086063336]
  We aim to integrate the structured and rule-based reasoning of algorithms with adaptive learning capabilities of neural networks. This dissertation provides theoretical and practical contributions to this area of research.
  arXiv  Detail & Related papers  (2024-02-21T12:16:51Z)
• Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review [1.1510009152620668]
  Rules offer a transparent and intuitive means of explaining neural networks. The study specifically addresses feedforward networks with supervised learning and crisp rules. Future work can extend to other network types, machine learning methods, and fuzzy rule extraction.
  arXiv  Detail & Related papers  (2023-12-20T09:40:07Z)
• What Planning Problems Can A Relational Neural Network Solve? [91.53684831950612]
  We present a circuit complexity analysis for relational neural networks representing policies for planning problems. We show that there are three general classes of planning problems, in terms of the growth of circuit width and depth. We also illustrate the utility of this analysis for designing neural networks for policy learning.
  arXiv  Detail & Related papers  (2023-12-06T18:47:28Z)
• Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
  Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data. We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data. NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
  arXiv  Detail & Related papers  (2022-05-25T12:41:59Z)
• Neural Architecture Search for Dense Prediction Tasks in Computer Vision [74.9839082859151]
  Deep learning has led to a rising demand for neural network architecture engineering. neural architecture search (NAS) aims at automatically designing neural network architectures in a data-driven manner rather than manually. NAS has become applicable to a much wider range of problems in computer vision.
  arXiv  Detail & Related papers  (2022-02-15T08:06:50Z)
• Weisfeiler and Leman go Machine Learning: The Story so far [41.069698435199705]
  We give a comprehensive overview of the Weisfeiler-Leman algorithm's use in a machine learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph- and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures.
  arXiv  Detail & Related papers  (2021-12-18T20:14:11Z)
• Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty [91.0564497403256]
  We present a novel framework that involves probabilistic fusion between the two families of predictions during network training. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN and ScanNet datasets.
  arXiv  Detail & Related papers  (2021-04-16T17:59:06Z)
• Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems [0.0]
  We consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.
  arXiv  Detail & Related papers  (2020-11-11T06:21:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.