Autonomous vehicle perception: The technology of today and tomorrow

By Jessica Van Brummelen, Marie O'Brien, Dominique Gruyer, Homayoun Najjaran. 2018

745 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “[20] X X X X X [21] X X X [22] X X [23] X [24] X X X X [25] X X X [26] X [27] X X X X X [28] X X X X Ours X X X X X X X X X”
Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China (2019, background): “In the long term, with the popularity of AV technology and the increase in AV penetration on the road, AVs and SAVs could dramatically reduce congestion caused by human driving behavior and improve the road transportation efficiency [10,14,15].”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “29/tesla-google-disagree-lidar-right/ [50] J.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “Using data directly from sensors or through sensor fusion techniques, the perception layer calculates the global and local location of the ego-vehicle and builds a map of the environment [1].”
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, background): “These two tasks need to acquire and process highly-accurate and information-rich data of real-world environments [3].”
Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019, background): “C REATING an accurate representation of the driving environment is the core function of the perception system of an autonomous vehicle and is crucial for safe operation of autonomous vehicles in complex environments [1].”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “[45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], scene understanding [68], [69], [70], tracking [71], [72],”
Artificial intelligence applications in the development of autonomous vehicles: a survey (2020, neutral): “Several approaches such as sensor fusion of camera, LIDAR, and radar sensors [45] and taillight recognition [46] were proposed.”
A Survey on 3D Object Detection Methods for Autonomous Driving Applications (2019, background): “A more comprehensive report on current sensors for AV applications can be found in [15] and [16].”