A Multi-sensor Traffic Scene Dataset with Omnidirectional Video

Koschorrek, Philipp; Piccini, Tommaso; Öberg, Peter; Felsberg, Michael; Nielsen, Lars; Mester, Rudolf

doi:10.1109/cvprw.2013.110

Cited by 16 publications

(17 citation statements)

References 5 publications

(11 reference statements)

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“…Automotive multisensor data set ( AMUSE ; Koschorrek et al, ): Provided by Linköping University of Sweden, it consists of sequences recorded in various environments from a car equipped with an omnidirectional multicamera, height sensors, an IMU, a velocity sensor, and a GPS. The application programming interface (API) for reading these data sets is provided to the public, together with a collection of long multisensor and multicamera data streams stored in the given format.…”

Section: Data Sources For Training Autonomous Driving Systemsmentioning

confidence: 99%

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A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,127

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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Section: Data Sources For Training Autonomous Driving Systemsmentioning

confidence: 99%

“…In the remaining of this section we will provide and highlight the distinctive characteristics of the most relevant data sets that are publicly available (Table 2). Automotive multisensor data set (AMUSE; Koschorrek et al, 2013):…”

Section: Safety Of Deep Learning In Autonomous Drivingmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,127

495

View full text Add to dashboard Cite

show abstract

“…In this paper, we attempt to supplement more driving datasets along with detailed descriptions and their open access status. Therefore, several existing datasets and their scale, source types, and potential applications are elaborated in this [141]. UAH-DriveSet is a publicly available dataset which was collected in 2016 by using a smartphone app DriveSafe for in-depth analysis of driving behaviors [28].…”

Section: A Utilization Of Existing Driving Dataset and Personal Datamentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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“…Comparison between different methods has been made easier recently thanks to a number of free datasets that have been made available [3], [26], [27], [28]. The KITTI dataset in particular is currently the most popular one, partly thanks to the benchmark test made available by the authors [3].…”

Section: Related Workmentioning

confidence: 99%

Robust stereo visual odometry from monocular techniques

Persson

Piccini

Felsberg

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Visual odometry is one of the most active topics in computer vision. The automotive industry is particularly interested in this field due to the appeal of achieving a high degree of accuracy with inexpensive sensors such as cameras. The best results on this task are currently achieved by systems based on a calibrated stereo camera rig, whereas monocular systems are generally lagging behind in terms of performance. We hypothesise that this is due to stereo visual odometry being an inherently easier problem, rather than than due to higher quality of the state of the art stereo based algorithms. Under this hypothesis, techniques developed for monocular visual odometry systems would be, in general, more refined and robust since they have to deal with an intrinsically more difficult problem.In this work we present a novel stereo visual odometry system for automotive applications based on advanced monocular techniques. We show that the generalization of these techniques to the stereo case result in a significant improvement of the robustness and accuracy of stereo based visual odometry. We support our claims by the system results on the well known KITTI benchmark, achieving the top rank for visual only systems * .

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A Multi-sensor Traffic Scene Dataset with Omnidirectional Video

Cited by 16 publications

References 5 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Robust stereo visual odometry from monocular techniques

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