AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities

Vial, Alphonse; Daamen, Winnie; Ding, Aaron Yi; Arem, Bart van; Hoogendoorn, Serge P.

doi:10.1109/mits.2019.2953509

Cited by 7 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5a, where the standard MHT misses the turn in absence of new updates and continues its prediction in the wrong direction (τ 1 1 -τ 1 5 ). With the arrival of new measurements, the MHT reacts by initiating a new track with along the new road segment (τ 3 6 -τ 3 7 ). The GOSPA performance is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Self-driving vehicles, drones, or other types of connected robots will enter populated environments and may act as mobile sensing platforms generating a proliferating amount of data about the platform's internal state, but also about the static and dynamic local area they observe. With the collective intelligence and innate mobility of such sensor platforms, pedestrians and cyclists traffic characteristics could be captured at an extended spatial and temporal scale [3]. In the future, knowledge about position, motion state, and pose of people could enable next generation traffic or crowd surveillance systems to estimate the number of people and reconstruct trajectories across the network.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Vial

Hendeby

Daamen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Vial

Hendeby

Daamen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…5, where the standard MHT misses the turn in absence of new updates and continues its prediction in the wrong direction (τ 1 1 -τ 1 5 ). With the arrival of new measurements, the MHT reacts by initiating a new track with along the new road segment (τ 3 6 -τ 3 7 ). The GOSPA performance is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Framework for Network-Constrained Target Tracking

Vial¹,

Hendeby²,

Daamen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The increase in perception capabilities of connected mobile sensor platforms (\eg, self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (\eg, pedestrians or cyclists) constrained by a road network, using mobile (\eg, vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a \emph{network-constrained multi-hypotheses tracker} (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.

show abstract

“…In [29], a theoretical pedestrian/cyclist mobile sensing system (AMSense) is proposed which describes the usage of vehicular sensors and V2X communication to capture spatiotemporal properties of pedestrians and cyclists. The system relies on vehicular sensors and has architectural elements where localized aggregation is applied with support from edge and cloud data processing.…”

Section: Pedestrian Density Measurementmentioning

confidence: 99%

“…change rates) on the error quantity, given a fixed set of mobility patterns in a dual corridor topography. We use a linear model of the form shown in (29) for the measurement values, where the ground truth measurement Ŷj (t) for a cell (*) Parameters varied in simulation studies. S0:3 mark different settings for each scenario.…”

Section: A Abstract Measurement Quantity (S0)mentioning

confidence: 99%

Cellular Sidelink Enabled Decentralized Pedestrian Sensing

2023

View full text Add to dashboard Cite

Pedestrian based mobile sensing enables a large number of urban-centric use cases in the areas of intelligent mobility, smart city and crowd management. With increasing standardization in Vehicle-to-Everything (V2X) communication to increase localized environmental awareness, i.e. cooperative perception (CP), a technological basis is already heavily discussed. Work in this area is usually directed towards road safety use cases. However, the same technologies could also be applied to pedestriancentric applications in urban areas. Use cases like spatio-temporal density maps of pedestrians for public transportation optimization or urban route planing are such examples. This paper introduces an opportunistic decentralized mobile crowd sensing (MCS) approach where arbitrary measurement quantities are collected, aggregated and disseminated in decentralized pedestrian measurement maps (DPMM). The sensing, dissemination, and aggregation are driven by mobile devices, without the need for centralized aggregation and dissemination infrastructure. By utilizing cellular sidelink communication (i.e. via the PC5 interface in 5G/6G systems) and node local aggregation, the perception of the environment can be directly shared with neighboring nodes. The described DPMM approach is evaluated using CrowNet, an open-source simulation framework based on OMNeT++/INET. The proposed approach is evaluated by several detailed simulation studies: first, using a synthetic measurement quantity with a linear change rate behavior and second, a real use case concerning decentralized pedestrian density measurements. The results indicate that DPMM can provide spatio-temporal maps of the local area with high level of detail and low delay -close to the optimum achievable in a specific mobility situation -while only requiring a moderate amount of cellular bandwidth.

show abstract

AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities

Cited by 7 publications

References 39 publications

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Framework for Network-Constrained Target Tracking

Cellular Sidelink Enabled Decentralized Pedestrian Sensing

Contact Info

Product

Resources

About