Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles

Morais, Gustavo Alves Prudencio de; Marcos, Lucas B.; Bueno, José Nuno A. D.; Resende, Nilo F. de; Terra, Marco H.; Grassi, Valdir

doi:10.1016/j.conengprac.2020.104630

Cited by 35 publications

(22 citation statements)

References 24 publications

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“…MDF is used to guarantee the robustness of information assimilated from numerous sensors in various driving situations. The missing information is formulated with the relationship among various accessible modalities [31].…”

Section: Multimodal Data Fusion (Mdf)mentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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Autonomous Driving Vehicle (ADV) services have become a prominent motif in intelligent vehicle technology by adapting deep learning features. Automated driverless services are a hercules task due to the dynamic driving environment and the performance is deliberately reliant on the quality of data fusion from sensors. Therefore, considering advanced 3D LiDAR sensors is essential to measure the surrounding with 360 • coverage. However, accomplishing maximum autonomy is the main challenge because of debilitated and complex driving environmenst. Deep Learning (DL) based models potentially impact surrounding measures for object detection, classification, and tracking. This study describes the importance of DL-LiDAR strategies to formulate ADV research challenges followed by a comprehensive analysis of Semantic Segmentation, Data Fusion, Data Representation, Feature Extraction, Dynamic Object Detection, and Autonomous Driving-Multi-Objective Tracking mechanisms. Unlike existing review papers, we examine various deep learning influenced approaches and describe their precarious measurements. Also, we analyze the impact of systematic methods based on open-source data sets for objectx (x=classification, localization, tracking) consolidation. Most of the summaries confine scalability, and feasibility of DL techniques in ADV to accomplish an intelligent driving system without human interference. Open research problems are discussed in the last section of the article.

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Section: Multimodal Data Fusion (Mdf)mentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…There are research studies that focus on efficient autonomous platooning with better fuel efficiency and better usage of road areas [8], [9]. Several researchers have proposed artificial intelligence (AI)-based solutions for the efficient organisation of vehicles [10]. Small vehicles designed to operate at a high speed are efficiently deployed to minimize air drag.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

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“…In terms of norm-bounded uncertainties, recursiveness has been explored in theoretical results and in implementations on actual systems, for instance, in [29]- [34]. It is also useful to join different decision and control approaches as deep learning VOLUME 4, 2016 and autonomous control systems to increase, for instance, the effectiveness of Internet of Things [35], and autonomous navigation in urban environments [36].…”

Section: Introductionmentioning

confidence: 99%

Robust Recursive Regulator for Systems Subject to Polytopic Uncertainties

2021

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We present a robust recursive framework for the regulation of discrete-time linear systems subject to polytopic uncertainties. Based on regularized least-squares with a penalty parameter, we formulate a convex optimization problem and weight the polytope vertices altogether. In this sense, the main contribution of this paper consists of a robust recursive framework for the computation of stabilizing feedback gains. The solution does not require numerical optimization packages and relies ultimately on a single penalty parameter which is easily tuned. Moreover, the gains are obtained recursively through algebraic expressions, as opposed to related works which employ linear matrix inequalities. Under observability and controllability conditions, we demonstrate convergence and stability of the closed-loop system in terms of an algebraic Riccati equation. We provide numerical and real-world examples to validate the proposed approach and for comparison with a robust H ∞ controller.

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Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles

Cited by 35 publications

References 24 publications

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Autonomous Convoying: A Survey on Current Research and Development

Robust Recursive Regulator for Systems Subject to Polytopic Uncertainties

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