New probabilistic approach to estimate vehicle failure trajectories in curve driving

• Statistical analysis for vehicle trajectories classification.
• Identification of non-Gaussian and Scalar normalized second order stochastic processes.
• Process approximation with a development on the basis of Hermite polynomials.
• Stochastic process simulation.
• Reliability analysis application and probability of failure estimation.

The vehicle trajectories analysis on dangerous bends is an important task to improve road safety. This paper proposes a new methodology to predict failure trajectories of light vehicles in curve driving. It consists to use a stochastic modelling and reliability analysis in order to estimate the failure probability of vehicle trajectories.Firstly, we build probabilistic models able to describe real trajectories in a given bend. The models are transforms of scalar normalized second order stochastic processes which are stationary, ergodic and non-Gaussian. The process is characterized by its probability density function and its power spectral density estimated starting from the experimental trajectories. The probability density is approximated by using a development on the basis of Hermite polynomials.The second part is devoted to apply a reliability strategy intended to associate a risk level to each class of trajectories. Based on the joint use of probabilistic methods for modelling uncertainties, reliability analysis for assessing risk levels and statistics for classifying the trajectories, this approach provides a realistic answer to the tackled problem. Experiments show the relevance and effectiveness of this method.