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Likely, Light, and ...
Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction
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- Rodrigues de Almeida, Tiago, 1996- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems (AASS)
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- Martinez Mozos, Oscar, 1974- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems (AASS)
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(creator_code:org_t)
- IEEE, 2023
- 2023
- Engelska.
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 24-28 Sept. 2023. - : IEEE. - 9798350399479 - 9798350399462 ; , s. 1269-1276
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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