Perception, Planning, Control, and Coordination for Autonomous Vehicles

Pendleton, Scott; Andersen, Hans Jørgen; Du, Xinxin; Shen, Xiaotong; Meghjani, Malika; Eng, You Hong; Rus, Daniela; Ang, Marcelo H.

doi:10.3390/machines5010006

Cited by 498 publications

(290 citation statements)

References 280 publications

(344 reference statements)

Supporting

Mentioning

278

Contrasting

Unclassified

Order By: Relevance

“…Such structures learn discriminative features from data automatically, giving them the ability to approximate very complex nonlinear relationships (BOX 1). While most earlier AI methods have led to applications with subhuman performance, recent deep learning algorithms are able to match and even surpass humans in task-specific applications 2–5 (FIG. 1).…”

mentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

View full text Add to dashboard Cite

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

show abstract

mentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The task of automated driving can be decomposed into the following subtasks: (i) defining the desired trajectory (driving line) and the desired pattern of speed along this trajectory, (ii) keeping the actual trajectory of the car as close as possible to the desired one, and (iii) maintaining the actual speed of the car along this trajectory as close as possible to the desired one [9,10]. We considered the first and third subtasks to be beyond the scope of our current work; instead, we focused on the second one-in challenging, slippery road conditions-which could be solved by an appropriate steering of the car.…”

Section: Introductionmentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

View full text Add to dashboard Cite

Abstract:The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

show abstract

“…We consider longterm reasoning as solved, as provided by navigation systems. These are standard methods for selecting a route through the road network, from the car's current position to destination (Pendleton et al, 2017). Learning-based control methods: Classical controllers make use of an a priori model composed of fixed parameters.…”

Section: Discussionmentioning

confidence: 99%

“…It addresses the taxonomy aspects of path planning, namely the mission planner, behavior planner, and motion planner. However, Pendleton et al () 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, ; Rehder, Quehl, & Stiller, ; Sun, Peng, Zhan, & Tomizuka, ) and DRL‐based planning (Paxton, Raman, Hager, & Kobilarov, ; L. Yu, Shao, Wei, & Zhou, ).…”

Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,214

495

View full text Add to dashboard Cite

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.

show abstract

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Cited by 498 publications

References 280 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

A survey of deep learning techniques for autonomous driving

Contact Info

Product

Resources

About