As recently stated by National Highway Traffic Safety Administration (NHTSA), to demonstrate the expected performance of a highly automated vehicles system, test approaches should include a combination of simulation, test track, and on-road testing. The simulation part need to be based on a probabilistic approach. To do so, an appropriate sampling strategy is often used. In this paper, we propose a new sampling strategy based on Markov Chain Monte Carlo (MCMC) methods, using Metropolis-Hastings algorithm to generate samples from probability distributions of Field Operational Tests (FOT); the Safety Pilot Model Deployment (SPMD) in our case. We begin by modeling the probability distribution of each test parameter retrieved from the SPMD database, two estimation methods were applied: Kernel Density Estimation method and EM algorithm. A comparison was made between the two methods to choose the best one. These distribution models are then sampled using our sampling strategy based on the Metropolis-Hastings algorithm.
CITATION STYLE
Chelbi, N. E., Gingras, D., & Sauvageau, C. (2018). New field operational tests sampling strategy based on metropolis-hastings algorithm. In Advances in Intelligent Systems and Computing (Vol. 868, pp. 1285–1302). Springer Verlag. https://doi.org/10.1007/978-3-030-01054-6_90
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