A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Papini, Gastone Pietro Rosati; Plebe, Alice; Lio, Mauro Da; Donà, Riccardo

doi:10.1109/tits.2021.3086397

Cited by 16 publications

(3 citation statements)

References 33 publications

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“…Predicting pedestrians' behaviors could contribute to the safe driving of autonomous vehicles. In [108], a safe speed network was constructed and integrated with the DRL agent. Moreover, a risk assessment was performed to predict the behaviors of distracted pedestrians.…”

Section: Vehicle-to-pedestrian Interactionmentioning

confidence: 99%

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Liu,

Li,

Tang

et al. 2023

Sensors

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The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

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Section: Vehicle-to-pedestrian Interactionmentioning

confidence: 99%

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Liu,

Li,

Tang

et al. 2023

Sensors

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“…Finally, the agent learns to maximize the reward obtained for task completion. Reinforcement learning has been studied and achieved remarkable results in many fields, such as chess playing 22 , 23 , robot control 24 – 26 , and autonomous driving 27 – 29 . In this paper, the long short-term memory (LSTM)-based proximal policy optimization (PPO) method of reinforcement learning is introduced into MIMO radar antenna placement optimization and compared with a GA and a PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Antenna Placement Optimization for Distributed MIMO Radar Based on a Reinforcement Learning Algorithm

Zhu,

Liu,

Zhang

et al. 2023

Sci Rep

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This paper studies an optimization problem of antenna placement for multiple heading angles of the target in a distributed multiple-input multiple-output (MIMO) radar system. An improved method to calculate the system’s coverage area in light of the changing target heading is presented. The antenna placement optimization problem is mathematically modelled as a sequential decision problem for compatibility with reinforcement learning solutions. A reinforcement learning agent is established, which uses the long short-term memory (LSTM)-based proximal policy optimization (PPO) method as the core algorithm to solve the antenna placement problem. Finally, the experimental findings demonstrate that the method can enhance the coverage area of antenna placement and thus has reference value for providing new ideas for the antenna placement optimization of distributed MIMO radar.

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“…ii) for autonomous driving (e.g. whether a pedestrian is distracted by the active use of an object and therefore more caution is required (Papini et al, 2021)).…”

Section: Evaluation Of Affordanceuptmentioning

confidence: 99%

Toward context-based text-to-3D scene generation

Henlein¹

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People can describe spatial scenes with language and, vice versa, create images based on linguistic descriptions. However, current systems do not even come close to matching the complexity of humans when it comes to reconstructing a scene from a given text. Even the ever-advancing development of better and better Transformer-based models has not been able to achieve this so far. This task, the automatic generation of a 3D scene based on an input text, is called text-to-3D scene generation. The key challenge, and focus of this dissertation, now relate to the following topics: (a) Analyses of how well current language models understand spatial information, how static embeddings compare, and whether they can be improved by anaphora resolution. (b) Automated resource generation for context expansion and grounding that can help in the creation of realistic scenes. (c) Creation of a VR-based text-to-3D scene system that can be used as an annotation and active-learning environment, but can also be easily extended in a modular way with additional features to solve more contexts in the future. (d) Analyze existing practices and tools for digital and virtual teaching, learning, and collaboration, as well as the conditions and strategies in the context of VR. In the first part of this work, we could show that static word embeddings do not benefit significantly from pronoun substitution. We explain this result by the loss of contextual information, the reduction in the relative occurrence of rare words, and the absence of pronouns to be substituted. But we were able to we have shown that both static and contextualizing language models appear to encode object knowledge, but require a sophisticated apparatus to retrieve it. The models themselves in combination with the measures differ greatly in terms of the amount of knowledge they allow to extract. Classifier-based variants perform significantly better than the unsupervised methods from bias research, but this is also due to overfitting. The resources generated for this evaluation are later also an important component of point three. In the second part, we present AffordanceUPT, a modularization of UPT trained on the HICO-DET dataset, which we have extended with Gibsonien/telic annotations. We then show that AffordanceUPT can effectively make the Gibsonian/telic distinction and that the model learns other correlations in the data to make such distinctions (e.g., the presence of hands in the image) that have important implications for grounding images to language. The third part first presents a VR project to support spatial annotation respectively IsoSpace. The direct spatial visualization and the immediate interaction with the 3D objects should make the labeling more intuitive and thus easier. The project will later be incorporated as part of the Semantic Scene Builder (SeSB). The project itself in turn relies on the Text2SceneVR presented here for generating spatial hypertext, which in turn is based on the VAnnotatoR. Finally, we introduce Semantic Scene Builder (SeSB), a VR-based text-to-3D scene framework using Semantic Annotation Framework (SemAF) as a scheme for annotating semantic relations. It integrates a wide range of tools and resources by utilizing SemAF and UIMA as a unified data structure to generate 3D scenes from textual descriptions and also supports annotations. When evaluating SeSB against another state-of-the-art tool, it was found that our approach not only performed better, but also allowed us to model a wider variety of scenes. The final part reviews existing practices and tools for digital and virtual teaching, learning, and collaboration, as well as the conditions and strategies needed to make the most of technological opportunities in the future.

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A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Cited by 16 publications

References 33 publications

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Antenna Placement Optimization for Distributed MIMO Radar Based on a Reinforcement Learning Algorithm

Toward context-based text-to-3D scene generation

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