Intelligent Land Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning

Yu, Lingli; Shao, Xuanya; Wei, Yadong; Zhou, Kaijun

doi:10.20944/preprints201808.0049.v1

Cited by 20 publications

(25 citation statements)

References 3 publications

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“…According to the path planning process, a self-driving car should consider all possible obstacles that are present in the surrounding environment and calculate a trajectory along a collision-free route. As stated in Shalev-Shwartz, Shammah, and Shashua DRL for path planning deals mainly with learning driving trajectories in a simulator (Panov, Yakovlev, & Suvorov, 2018;Paxton et al, 2017;Shalev-Shwartz et al, 2016;L. Yu et al, 2018).…”

Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

“…However, Pendleton et al (2017) do not include a review on deep learning technologies, although the state-of-the-art literature has revealed an increased interest in using deep learning technologies for path planning and behavior arbitration. Following, we discuss two of the most representative deep learning paradigms for path planning, namely IL (Grigorescu, Trasnea, Marina, Vasilcoi, & Cocias, 2019; Rehder, Quehl, & Stiller, 2017; Sun, Peng, Zhan, & Tomizuka, 2018) and DRL-based planning(Paxton, Raman, Hager, & Kobilarov, 2017;L. Yu, Shao, Wei, & Zhou, 2018).The goal in ILRehder et al, 2017;Sun et al, 2018) is to learn the behavior of a human driver from recorded driving experiences(Schwarting, Alonso-Mora, & Rus, 2018).…”

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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

1,136

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: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

mentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,136

495

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“…Owing to impressive advantages in extracting features, deep learning is promising to overcome the bottlenecks of conventional planning algorithms and learn to plan paths efficiently under various conditions for self-driving cars. For example, Yu et al [79] proposed a path planning method based on deep reinforcement learning. This method can deal with the problem of the model training with continuous input and output.…”

Section: Path Planningmentioning

confidence: 99%

“…The flow chart of the method proposed in [79] is shown in Figure 18. There are an actor policy network and a critic evaluation network.…”

Section: Path Planningmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

111

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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“…A deep learning path planning algorithm for autonomous driving is proposed in [6]. The method addresses the problem of error modeling and path tracking dependencies.…”

Section: Related Workmentioning

confidence: 99%

NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles

Grigorescu

Trasnea

Marina

et al. 2019

IEEE Robot. Autom. Lett.

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Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL) systems. Current deep learning solutions for autonomous driving are subject to several limitations (e.g. they estimate driving actions through a direct mapping of sensors to actuators, or require complex reward shaping methods). Although the cost function used for training can aggregate multiple weighted objectives, the gradient descent step is computed by the backpropagation algorithm using a singleobjective loss. To address these issues, we introduce NeuroTrajectory, which is a multi-objective neuroevolutionary approach to local state trajectory learning for autonomous driving, where the desired state trajectory of the ego-vehicle is estimated over a finite prediction horizon by a perception-planning deep neural network. In comparison to DRL methods, which predict optimal actions for the upcoming sampling time, we estimate a sequence of optimal states that can be used for motion control. We propose an approach which uses genetic algorithms for training a population of deep neural networks, where each network individual is evaluated based on a multi-objective fitness vector, with the purpose of establishing a so-called Pareto front of optimal deep neural networks. The performance of an individual is given by a fitness vector composed of three elements. Each element describes the vehicle's travel path, lateral velocity and longitudinal speed, respectively. The same network structure can be trained on synthetic, as well as on real-world data sequences. We have benchmarked our system against a baseline Dynamic Window Approach (DWA), as well as against an End2End supervised learning method.

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Intelligent Land Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning

Cited by 20 publications

References 3 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles

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