Density-based Stochastic Reachability Computation for Occupancy Prediction in Automated Driving
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Abstract (arXiv)
We propose a stochastic reachability computation framework for occupancy prediction in automated driving by directly solving the underlying transport partial differential equation governing the advection of the closed-loop joint density functions. The resulting nonparametric gridless computation is based on integration along the characteristic curves, and allows online computation of the time-varying collision probabilities. Numerical simulations highlight the scope the proposed method.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2020.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023Path Structured Multimarginal Schr"odinger Bridge for Probabilistic Learning of Hardware Resource Usage by Control Softwarecited by 3
- 2024Solution of the Probabilistic Lambert Problem: Connections with Optimal Mass Transport, Schr"odinger Bridge and Reaction-Diffusion PDEscited by 3
- 2024Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schr"odinger Bridgecited by 3
- 2024Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schr"odinger Bridge21 May 2024
- 2024Solution of the Probabilistic Lambert Problem: Connections with Optimal Mass Transport, Schr"odinger Bridge and Reaction-Diffusion PDEs15 Jan 2024
- 2023Path Structured Multimarginal Schr"odinger Bridge for Probabilistic Learning of Hardware Resource Usage by Control Software1 Oct 2023