The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections

Bock, Julian; Krajewski, Robert; Moers, Tobias; Runde, Steffen; Vater, Lennart; Eckstein, Lutz

doi:10.1109/iv47402.2020.9304839

Cited by 241 publications

(168 citation statements)

References 13 publications

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“…First, to apply the proposed vector-based approach at large scale, suitable and reasonable sets of delay functions are required and an assessment is necessary on which delay functions are best applicable. Consequently, future research should develop different delay functions and assess their performance and suitability for modeling traffic at large urban scale, e.g., by using large-scale drone data (e.g., Barmpounakis and Geroliminis, 2020;Bock et al, 2020). Second, the proposed tri-modal MFD is clearly context specific and implications thereof may be altered when applied to a different context and network topology.…”

Section: Discussionmentioning

confidence: 99%

How Many Cars in the City Are Too Many? Towards Finding the Optimal Modal Split for a Multi-Modal Urban Road Network

Loder

Bressan

Wierbos

et al. 2021

Front. Future Transp.

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Interactions among different modes or vehicle classes in urban road networks affect the network performance in different and complex ways. Thus, an answer to the question of “how many cars are too many for a city?” is not trivial. However, multi-modal macroscopic fundamental diagrams (MFD) offer a novel opportunity to answer this question. So far, no methodology exists to estimate multi-modal MFDs resulting from arbitrary multi-modal interactions. In this paper, we propose a methodology to capture additional delays in the shape of the MFD and derive an approach for estimating multi-modal MFDs thereof. The influence on the MFD shape is established using the two-fluid theory of urban traffic by defining pairwise copula functions between travel times of each mode. In contrast to many existing approaches, the presented approach retains individual mode's speed information. We show the applicability of the approach with a tri-modal case of bicycles, buses, and cars with empirical data from Amsterdam (The Netherlands) and London (United Kingdom). Although the approach is not limited to this specific tri-modal case, we use the example to discuss the initial policy question by deriving optimal modal splits for a given accumulation of travelers. Last, we compare the new approach to existing estimation methods for bi-modal MFDs describing car and bus traffic.

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Section: Discussionmentioning

confidence: 99%

How Many Cars in the City Are Too Many? Towards Finding the Optimal Modal Split for a Multi-Modal Urban Road Network

Loder

Bressan

Wierbos

et al. 2021

Front. Future Transp.

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“…The inD dataset [108], which is achieved using a static drone, includes over 11 K trajectories of road users, typically motorized agents. The scenarios are based on urban mobility, including scenes of road intersections or roundabouts.…”

Section: Traffic Capturementioning

confidence: 99%

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea

Stanciu

Căleanu

2021

Sensors

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Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

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“…A variety of data sources are used in the detection of intersections: images [ 87 ], map tiles [ 86 ], videos [ 88 ], LiDAR sensors [ 85 ], and vehicle trajectories [ 89 , 90 ]. Here, computer vision approaches will be discussed as images and videos are considered a rich source of information, providing detailed junction information, such as the number of lanes.…”

Section: Environment Mappingmentioning

confidence: 99%

A Systematic Review of Urban Navigation Systems for Visually Impaired People

El-taher

Taha

Courtney

et al. 2021

Sensors

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Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.

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The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections

Cited by 241 publications

References 13 publications

How Many Cars in the City Are Too Many? Towards Finding the Optimal Modal Split for a Multi-Modal Urban Road Network

How Many Cars in the City Are Too Many? Towards Finding the Optimal Modal Split for a Multi-Modal Urban Road Network

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

A Systematic Review of Urban Navigation Systems for Visually Impaired People

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