2 citations · 2 across the 1 of their papers we have counts for
4 papers
A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the…
Identification of Driving Heterogeneity using Action-chains
Xue Yao, Simeon C. Calvert, Serge P. Hoogendoorn
Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driv…
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles
Guopeng Li, Yiru Jiao, Victor L. Knoop +2
Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently w…
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
This paper proposes a new framework to compute finite-horizon safety guarantees for discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The appr…