Prediction and retrodiction algorithms for path-constrained targets

Krishnan, Krishanth; Kirubarajan, Thiagalingam; Valin, Pierre; Meger, Eric

doi:10.1109/taes.2014.120716

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Reference [13] extends the idea of including road map knowledge to random finite set (RFS) methods, however, using a more involved road representation and handling than the method proposed in this paper. Reference [14] also uses a different road and coordinate representation that requires projections, however, the concept of branching hypotheses in ambiguous situations (e.g., at intersections), also found in [15], is similar to the handling found in our approach.…”

mentioning

confidence: 85%

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Vial

Hendeby

Daamen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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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.

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mentioning

confidence: 85%

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

Vial

Hendeby

Daamen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Cars and trucks are limited to roads [19], [20], trains are limited to their tracks [3], and even ships are partially limited to rivers or stick to shipping lanes in the open oceans [21]. Knowledge of these paths (i.e.…”

Section: B Map-aided Localizationmentioning

confidence: 99%

Path-Constrained State Estimation for Rail Vehicles

Von Einem,

Cramariuc,

Siegwart

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Globally rising demand for transportation by rail is pushing existing infrastructure to its capacity limits, necessitating the development of accurate, robust, and highfrequency positioning systems to ensure safe and efficient train operation. As individual sensor modalities cannot satisfy the strict requirements of robustness and safety, a combination thereof is required. We propose a path-constrained sensor fusion framework to integrate various modalities while leveraging the unique characteristics of the railway network. To reflect the constrained motion of rail vehicles along their tracks, the state is modeled in 1D along the track geometry. We further leverage the limited action space of a train by employing a novel multi-hypothesis tracking to account for multiple possible trajectories a vehicle can take through the railway network. We demonstrate the reliability and accuracy of our fusion framework on multiple tram datasets recorded in the city of Zurich, utilizing Visual-Inertial Odometry for local motion estimation and a standard GNSS for global localization. We evaluate our results using ground truth localizations recorded with a RTK-GNSS, and compare our method to standard baselines. A Root Mean Square Error of 4.78 m and a track selectivity score of up to 94.9 % have been achieved.

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