How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, evaluate maneuver of scenario, surrogate models (SMs) are often used without explicit knowledge CAV under test. However, performance dissimilarities between SM test usually exist, it can lead generation suboptimal libraries. this article, an ada...

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, validate its effectiveness in principled way. To model generate scenarios trajectories with different lengths, we develop two approaches. First, adapt Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on length trajectories. This provides us flexibility to ...

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

Testing and evaluation is a critical step in the development deployment of connected automated vehicles (CAVs), yet there no systematic framework to generate testing scenario library. This study aims provide general for library generation (TSLG) problem with different operational design domains (ODDs), CAV models, performance metrics. Given an ODD, defined as set scenarios that can be used test...

This paper formed part of a preliminary research report for a risk consultancy and academic research. Stochastic Programming models provide a powerful paradigm for decision making under uncertainty. In these models the uncertainties are represented by a discrete scenario tree and the quality of the solutions obtained is governed by the quality of the scenarios generated. We propose a new techni...