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pro vyhledávání: '"Chib A"'
With recent advancements in microelectromechanical systems, low-power integrated circuits, and wireless communications, wireless sensor networks have gained immense significance [1][2]These distributed networks facilitate the efficient utilization of
Externí odkaz:
http://arxiv.org/abs/2411.15690
Autor:
Chib, Pranav Singh, Singh, Pravendra
Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajecto
Externí odkaz:
http://arxiv.org/abs/2411.00174
Autor:
Chib, Pranav Singh, Singh, Pravendra
Accurately predicting future pedestrian trajectories is crucial across various domains. Due to the uncertainty in future pedestrian trajectories, it is important to learn complex spatio-temporal representations in multi-agent scenarios. To address th
Externí odkaz:
http://arxiv.org/abs/2406.00749
In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2
Externí odkaz:
http://arxiv.org/abs/2404.13986
Publikováno v:
Review of Technologies and Disruptive Business Strategies
Autor:
Chib, Pranav Singh, Singh, Pravendra
Accurate pedestrian trajectory prediction is crucial for various applications, and it requires a deep understanding of pedestrian motion patterns in dynamic environments. However, existing pedestrian trajectory prediction methods still need more expl
Externí odkaz:
http://arxiv.org/abs/2403.08032
Autor:
Chib, Pranav Singh, Singh, Pravendra
Trajectory prediction is an important task that involves modeling the indeterminate nature of traffic actors to forecast future trajectories given the observed trajectory sequences. However, current methods confine themselves to presumed data manifol
Externí odkaz:
http://arxiv.org/abs/2312.09466
Autor:
Chib, Pranav Singh, Singh, Pravendra
The inherently diverse and uncertain nature of trajectories presents a formidable challenge in accurately modeling them. Motion prediction systems must effectively learn spatial and temporal information from the past to forecast the future trajectori
Externí odkaz:
http://arxiv.org/abs/2309.17338
Autor:
Chib, Siddhartha, Shimizu, Kenichi
A common assumption in the fitting of unordered multinomial response models for $J$ mutually exclusive categories is that the responses arise from the same set of $J$ categories across subjects. However, when responses measure a choice made by the su
Externí odkaz:
http://arxiv.org/abs/2308.12470
Autor:
Chib, Pranav Singh, Singh, Pravendra
End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems, such as their overwhelming complexity and propensity for error propagation. Autonomous driving transcends conventional traffic patterns by pro
Externí odkaz:
http://arxiv.org/abs/2307.04370