A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

Ye, Fei; Zhang, Shen; Pin, Wang; Chan, Ching-Yao

doi:10.48550/arxiv.2105.14218

Cited by 5 publications

(5 citation statements)

References 55 publications

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“…Ye et al [824] provide an overview of recent methods on RL-based planning methods. They separate methods into end-to-end systems, based on sensor data as input, and motion planning modules as a follow-up module of a perception stage.…”

Section: Applicationsmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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Section: Applicationsmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As a subset of machine learning, reinforcement learning (RL) has been extensively applied to solve various decision making and control problems in a data-driven fashion. Specifically, RL is able to learn in a trial-and-error way and does not require explicit human labeling or supervision on each data sample [232]. Instead, it requires a well-defined reward function to obtain reward signals throughout its learning process.…”

Section: Deep Reinforcement Learning-enabled Next Generation Electric...mentioning

confidence: 99%

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

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“…An execution layer is in charge of the motion, while a decision-making layer executes the high-level actions. This is known as the hierarchical approach [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Autonomous Driving at Intersections in CARLA Simulator

Gutiérrez-Moreno

Barea

López-Guillén

et al. 2022

Sensors

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Intersections are considered one of the most complex scenarios in a self-driving framework due to the uncertainty in the behaviors of surrounding vehicles and the different types of scenarios that can be found. To deal with this problem, we provide a Deep Reinforcement Learning approach for intersection handling, which is combined with Curriculum Learning to improve the training process. The state space is defined by two vectors, containing adversaries and ego vehicle information. We define a features extractor module and an actor–critic approach combined with Curriculum Learning techniques, adding complexity to the environment by increasing the number of vehicles. In order to address a complete autonomous driving system, a hybrid architecture is proposed. The operative level generates the driving commands, the strategy level defines the trajectory and the tactical level executes the high-level decisions. This high-level decision system is the main goal of this research. To address realistic experiments, we set up three scenarios: intersections with traffic lights, intersections with traffic signs and uncontrolled intersections. The results of this paper show that a Proximal Policy Optimization algorithm can infer ego vehicle-desired behavior for different intersection scenarios based only on the behavior of adversarial vehicles.

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A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

Cited by 5 publications

References 55 publications

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Reinforcement Learning-Based Autonomous Driving at Intersections in CARLA Simulator

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