Results 231 to 240 of about 470,864 (273)
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Addressing the uncertainties in autonomous driving
SIGSPATIAL Special, 2016Autonomous driving is a highly complex sensing and control problem. Today's vehicles may include many different compositions of sensor sets including the newer more sophisticated sensors like radar, cameras, and lidar. Each sensor in the car provides specific information about the environment at varying levels and has an inherent uncertainty and ...
Jane MacFarlane, Matei Stroila
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Intrusion-Tolerant Autonomous Driving
2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), 2018Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety.
Marcus Völp +1 more
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Visual routines for autonomous driving
Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 2002The paper describes visual routines based on models of color and shape, as well as crucial issues involving the scheduling of such routines. The visual routines are developed in a unique platform. The view from a car driving in a simulated world is feed into a Datacube pipeline video processor. The use of this simulation provides a flexible environment
Garbis Salgian, Dana H. Ballard
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Deep Learning for Autonomous Driving
2019 Digital Image Computing: Techniques and Applications (DICTA), 2019In this paper we look at Deep Learning methods using TensorFlow for autonomous driving tasks. Using scale model vehicles in a traffic scenario similar to the Audi Autonomous Driving Cup and the Carolo Cup, we successfully used Deep Learning stacks for the two independent tasks of lane keeping and traffic sign recognition.
Nicholas Burleigh +2 more
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Autonomous Driving Car Competition
2019This paper presents the construction of an autonomous robot to participating in the autonomous driving competition of the National Festival of Robotics in Portugal, which relies on an open platform requiring basic knowledge of robotics, like mechanics, control, computer vision and energy management.
João Pedro Alves +4 more
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Computer Architectures for Autonomous Driving
Computer, 2017To enable autonomous driving, a computing stack must simultaneously ensure high performance, consume minimal power, and have low thermal dissipation—all at an acceptable cost. An architecture that matches workload to computing units and implements task time-sharing can meet these requirements.
Shaoshan Liu +3 more
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The ApolloScape Dataset for Autonomous Driving
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece.
Xinyu Huang 0001 +7 more
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Technological Challenges in Autonomous Driving
Proceedings of the 1st International Workshop on Communication and Computing in Connected Vehicles and Platooning, 2018Automotive world is quickly moving towards another revolution of driverless cars. To realize this dream in large scale will see lots of challenges in terms of product development. In this talk, I would like to address different problems which automotive industry will have to face to make this a reality.
A. K. Prakash, Hrishikesh Venkataraman
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Adaptive System for Autonomous Driving
2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2018Avoiding faults in systems is of uttermost importance during system development. In case of autonomous systems this requirement becomes more important because of the fact that there is no human in the loop that can take over control after a fault. In this paper, we discuss a methodology allowing to implement a system that reacts on faults in a smart ...
Franz Wotawa, Martin Zimmermann 0007
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Dynamic representations for autonomous driving
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017This paper presents a method for observational learning in autonomous agents. A formalism based on deep learning implementations of variational methods and Bayesian filtering theory is presented. It is explained how the proposed method is capable of modeling the environment to mimic behaviors in an observed interaction by building internal ...
Juan Sebastian Olier +6 more
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