Transferable Pedestrian Motion Prediction Models at Intersections

Shen, Macheng; Habibi, Golnaz; How, Jonathan P.

doi:10.48550/arxiv.1804.00495

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…However, many of those methods either need additional training input in new environments or experience transfer issues. To the best of our knowledge, only a few methods explicitly highlight the performance in new environments outside the training scenario [10], [28], [29], [30]. As we will later describe, our approach explicitly uses only semantic maps as input and there will be no need to adapt the learned models to new environments, described with the same semantic classes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Rudenko¹,

Palmieri²,

Doellinger³

et al. 2021

Preprint

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Section: Related Workmentioning

confidence: 99%

“…Several Inverse Reinforcement Learning (IRL, or Inverse Optimal Control, IOC) approaches make use of semantic maps for predicting future human motion [9], [10], [30]. In particular, they use the semantic maps for encoding the features of the reward function.…”

Section: Related Workmentioning

confidence: 99%

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Rudenko¹,

Palmieri²,

Doellinger³

et al. 2021

Preprint

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“…On the other extreme, for cases like autonomous driving, the change of environment is connatural to the domain (a moving vehicle constantly changing its location), and the parameters of these models are abundant and highly variable. Thus, these applications need transferable solutions, transferability that is specifically sought by researchers, for instance in LIDAR based localization [157] or pedestrian motion estimation [158]. In any case, and regardless the domain, ITS research is in an incipient stage (probably with the exception of autonomous driving) of developing transferable models, and evaluating this feature, and some machine learning paradigms can help improve this characteristic.…”

Section: E Transferabilitymentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

Laña¹,

Sánchez-Medina²,

Vlahogianni³

et al. 2020

Preprint

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Advances in Data Science permeate every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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“…Inverse planning methods estimate the reward function or action model from observed trajectories using statistical learning techniques, e.g. (Ziebart et al 2009;Kitani et al 2012;Rehder et al 2018;Kuderer et al 2012;Pfeiffer et al 2016;Chung and Huang 2012;Shen et al 2018;Lee et al 2017;Walker et al 2014;Huang et al 2016) Figure 4 shows the publications trends over the last years, color-coded by modeling approach. The number of related works is strongly increasing during the last five years in particular for pattern-and planning-based methods.…”

Section: Modeling Approachmentioning

confidence: 99%

“…All the detailed methods show that IRL or similar methods are providing powerful tools to learn human behaviors. Furthermore, Shen et al (2018) show that under some particular requirements (i.e. when the feature vector, model parameter and output representation are invariant under a rigid body transformation of the world fixed coordinate frame), IRL is suitable for learning location-independent transferable motion models.…”

Section: Single-agent Inverse Planningmentioning

confidence: 99%

Human Motion Trajectory Prediction: A Survey

Rudenko,

Palmieri,

Herman

et al. 2019

Preprint

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

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Transferable Pedestrian Motion Prediction Models at Intersections

Cited by 3 publications

References 11 publications

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

Human Motion Trajectory Prediction: A Survey

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