Towards fully autonomous driving: Systems and algorithms

By Jesse Levinson, Jake Askeland, J. Becker, Jennifer Dolson, David Held, Sören Kammel, J. Kolter, D. Langer, Oliver Pink, V. Pratt and 6 more. 2011

1,381 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition, Robotics & Control

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Citations in the corpus

Concrete Problems in AI Safety (2016, neutral): “A I] 2 1 Ju n”
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”
Textual Explanations for Self-Driving Vehicles (2018, neutral): “Notable examples include [23], [4], and [16].”
An Empirical Evaluation of Deep Learning on Highway Driving (2015, methodology): “We follow the process descried in [5] to create LIDAR maps of the environment using the Velodyne and GNSS systems.”
Enhancements of V2X communication in support of cooperative autonomous driving (2015, background): “Most autonomous vehicles currently in development are based on a perception subsystem consisting of on-board sensors, which build a map of the vehicle’s environment, and a control subsystem that governs the longitudinal and lateral motion of the vehicle [1], [2], [3].”
Self-Driving Cars: A Survey (2019, methodology): “Levinson et al. (2011) employed a mixture of a Model Pre-”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “The DARPA challenges are a milestone for the prototyping of autonomous driving vehicles, including Boss from CMU [21], Junior from Stanford [22], TerraMax and BRAiVE from University of Parma [23], etc.”
Security vulnerabilities of connected vehicle streams and their impact on cooperative driving (2015, methodology): “Levinson et al. [1] present algorithms used in “Junior” –Stanford’s autonomous vehicle that is capable of performing complex driving tasks in real time in unpredictable traffic conditions.”
Massively parallel coherent laser ranging using a soliton microcomb (2019, background): “In recent years there has been a major interest in LIDAR fueled by the emergent development of autonomous driving [2], which require the ability to quickly recognize and classify objects in fast-changing and low visibility conditions [13].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, positive): “of the Stanford’s vehicle from the Urban Challenge in [266].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “Object recognition, like identifying road markings, pedes-trians, bicyclists and cars [35], can be achieved by lidar.”
Traffic sign recognition — How far are we from the solution? (2013, background): “The interest is driven by the market for intelligent applications such as autonomous driving [1], advanced driver assistance systems (ADAS) [2], mobile mapping [3], and the recent releases of larger traffic signs datasets such as Belgian [3] or German [4] datasets.”
Regularizing Neural Networks via Adversarial Model Perturbation (2020, background): “Deep neural networks without any regularizer are prone to overconfidence on incorrect predictions, and a well calibrated network is required especially in some application areas like object detection [12, 36] and autonomous vehicle control [5, 30].”
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis (2013, neutral): “Lidar has been extensively used for obstacle detection in autonomous vehicles [9] and are beginning to make their way into driver assistance applications such as ACC [10].”