Towards a viable autonomous driving research platform

By Junqing Wei, Jarrod M. Snider, Junsung Kim, John M. Dolan, R. Rajkumar, Bakhtiar Litkouhi. 2013

266 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “and radars lack the necessary resolution for many perception tasks [47].”
A multi-sensor fusion system for moving object detection and tracking in urban driving environments (2014, methodology): “After the Urban Challenge, Carnegie Mellon University started a new effort to advance the findings of the Urban Challenge and developed a new autonomous vehicle [20] to fill the gap between the experimental robotic vehicles and consumer cars.”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, methodology): “Components of modular pipeline Preception: The perception module seeks to achieve a better understanding [32] of the scene.”
Self-Driving Cars: A Survey (2019, background): “In 2013, the Carnegie Mellon University tested their driverless car [WEI13] on public roads for over a year.”
Making Bertha Drive—An Autonomous Journey on a Historic Route (2014, methodology): “The Defense Advanced Research Projects Agency (DARPA) organized a first Grand Challenge for autonomous off-road ground vehicles in March 2004 and a second challenge in October 2005, e.g. [5].”
MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach (2018, background): “In addition, connectivity between vehicles allows exchange of such information, which equips the vehicles with situational awareness [10], [11].”
Artificial intelligence applications in the development of autonomous vehicles: a survey (2020, background): “To learn from drivers and extract key factors, RL approaches have also been explored [37], [68]–[75].”
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving (2019, background): “Among these various types of sensors, a camera sensor can accurately identify the object type based on texture and color features and is more cost-effective [24] than other sensors.”
Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs (2016, background): “Academia has also demonstrated meaningful results by running autonomous driving tests.(9,10) In the future, as addressed in Kumar et al.”
Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology (2015, background): “It was a pioneer in the field of vehicular robotics, since it was totally autonomous (Wei, 2013; Broggi, 2013).”
Online Verification of Automated Road Vehicles Using Reachability Analysis (2014, methodology): “The hardware design of the new vehicle differs from the previous one by hiding sensors and computing devices, which results in a more production-ready vehicle (see [59] and Fig.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “s: modular-based and end-to-end based. Modular design decouples the localization, perception, control, etc. as separate modules and make it possible for people with different background work together [14], [15], [16], [17], [18]. End-toend based design is largely motivated by the development of arti”