End-to-End Race Driving with Deep Reinforcement Learning

Jaritz, Maximilian; Charette, Raoul de; Toromanoff, Marin; Pérot, Etienne; Nashashibi, Fawzi

doi:10.1109/icra.2018.8460934

Cited by 172 publications

(122 citation statements)

References 21 publications

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“…Although continuous control with DRL is possible, the most common strategy for DRL in autonomous driving is based on discrete control (Jaritz, Charette, Toromanoff, Perot, & Nashashibi, ). The main challenge here is the training, since the agent has to explore its environment, usually through learning from collisions.…”

Section: Overview Of Deep Learning Technologiesmentioning

confidence: 99%

“…End2End control papers mainly employ either deep neural networks trained offline on real‐world and/or synthetic data (Bechtel et al, ; Bojarski et al, ; C. Chen, Seff, Kornhauser, & Xiao, ; Eraqi et al, ; Fridman et al, ; Hecker et al, ; Rausch et al, ; Xu et al, ; S. Yang et al, ), or DRL systems trained and evaluated in simulation (Jaritz et al, ; Perot, Jaritz, Toromanoff, & Charette, ; Sallab et al, 2017b). Methods for porting simulation trained DRL models to real‐world driving have also been reported (Wayve, 2018), as well as DRL systems trained directly on real‐world image data (Pan et al, , ).…”

Section: Motion Controllers For Ai‐based Self‐driving Carsmentioning

confidence: 99%

mentioning

confidence: 99%

“…Chen, Seff, Kornhauser, & Xiao, 2015;Eraqi et al, 2017;Fridman et al, 2017;Hecker et al, 2018;Rausch et al, 2017;Xu et al, 2017;S. Yang et al, 2017a), or DRL systems trained and evaluated in simulation(Jaritz et al, 2018;Perot, Jaritz, Toromanoff, & Charette, 2017;Sallab et al, 2017b). Methods for porting simulation trained DRL models to real-world driving have also been reported (Wayve, 2018), as well as DRL systems trained directly on real-world image data(Pan et al, 2017(Pan et al, , 2018.End2End methods have been popularized in the last couple of years by NVIDIA ® , as part of the PilotNet architecture.…”

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confidence: 99%

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A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,214

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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Section: Overview Of Deep Learning Technologiesmentioning

confidence: 99%

Section: Motion Controllers For Ai‐based Self‐driving Carsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,214

495

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show abstract

“…The success of these applications are mainly due to the fact that a large amount of datasets has become available in the last years. In the context of intelligent vehicles, interesting work has been developed in several different fields: trajectory prediction [5] [6], mapping [7], control [10] and even end-to-end approaches [8] [9], where the car is controlled completely by a Deep Learning module.…”

Section: Related Workmentioning

confidence: 99%

An LSTM Network for Real-Time Odometry Estimation

Valente

Joly

Fortelle

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.

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Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

Wang

Luo

et al. 2020

Concurrency and Computation

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Autonomous obstacle avoidance control of unmanned surface vessels (USVs) in complex marine environments is always fundamental for its scientific search and detection. Traditional methods usually model USV motion and environments in a mathematical way that needs perceptual information. Unfortunately, it is difficult to provide sufficient perceptual information due to complex marine environments, resulting in inaccurate modeling. Reinforcement learning has recently enjoyed increasing popularity in the problem of obstacle avoidance since it can settle problems by partially observable environment information. However, the autonomous USV obstacle avoidance using reinforcement learning is still facing the challenge of designing appropriate reward functions under complex marine environments. To address these issues, we propose a prior knowledge‐based USV reinforcement learning obstacle avoidance algorithm. In this algorithm, an actor‐critic network is used as the main architecture of the algorithm, and prior knowledge‐based reward shaping used to design relevant reward function for USV obstacle avoidance. A standard USV based on a visual sensor is designed, and the state input of the algorithm is through USV's front vision sensor. We conducted simulation experiments and results prove that our algorithm can effectively converge, and USV achieves high velocity and low collision rate in the complex marine environment.

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End-to-End Race Driving with Deep Reinforcement Learning

Cited by 172 publications

References 21 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

An LSTM Network for Real-Time Odometry Estimation

Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

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