Perception, Planning, Control, and Coordination for Autonomous Vehicles

By Scott Drew Pendleton, Hans Andersen, Xinxin Du, Xiaotong Shen, Malika Meghjani, You Hong Eng, Daniela Rus, Marcelo H. Ang Jr.. 2017

645 citations recorded by the source corpus.

Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efﬁciency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/eb26de17a463dfe6fa655ef1362dfd46e86fda23

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In Automotive Engineering

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

A General Pipeline for 3D Detection of Vehicles (2018, background): “A detailed review of LiDAR approaches can be found in [14].”
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing (2022, methodology): “For this survey we want to display both software and hardware efforts in the field of autonomous racing and therefore we use the following perception - planning - control pipeline [235] depicted in”
Autonomous Cars: Research Results, Issues, and Future Challenges (2019, background): “In other words, autonomous vehicle control is a module that controls the behavior of the autonomous car in different situations and environments and guide its execution [84].”
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “Autonomous driving has seen dramatic advances in recent years, for instance for road scene parsing [1,2,3,4], lane following [5,6,7], path planning [8,9,10,11], and end-toend driving models [12,13,14,15].”
Artificial intelligence in radiology (2018, neutral): “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.”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, methodology): “MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation [257] Perception, Planning, Control, and Coordination for Autonomous Vehicles [258] Survey of neural networks in autonomous driving [259] Self-Driving Cars: A Survey [”
Networking and Communications in Autonomous Driving: A Survey (2019, background): “The core functions of existing autonomous vehicles are mainly actualized by three parts: perception, planning and control [3].”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “The core functions of autonomous driving include perception, planning, and control [32].”
Synthetic Data for Deep Learning (2019, neutral): “ [177,346,411,435], unmanned aerial vehicles [11,126,305], and more; see also general surveys of computer vision for mobile robot navigation [68,142] and perception and control for autonomous driving [443]. Before proceeding to current state of the art, we note an interesting historical fact: one of”
A Survey on 3D Object Detection Methods for Autonomous Driving Applications (2019, background): “In [8], on-road object detection was briefly reviewed among other perception functions, however, authors predominantly considered 2D object”
A survey of deep learning techniques for autonomous driving (2019, methodology): “These are standard methods for selecting a route through the road network, from the car’s current position to destination (Pendleton et al., 2017).”