Two-way collinear interaction of longitudinal waves in an elastic medium with quadratic nonlinearity

A numerical implementation of two-way collinear interaction of nonlinear ultrasonic longitudinal waves in an elastic medium with quadratic nonlinearity is conducted in this work. A semi-discrete central scheme is used here to solve the numerical problem. The pulse-inversion technique is applied to accentuate the generated resonant waves and remove the fundamental components. The produced resonant waves can be clearly observed in the frequency domain. Variation trends of the resonant waves together with second harmonics along the propagation path are analyzed and results show that apart from the obvious growing of the transverse component with difference frequency, the longitudinal component and the resonant wave of sum frequency have notable responses as well. The spatial distribution of resonant waves will provide necessary information for the related experiments

CAT: Closed-loop Adversarial Training for Safe End-to-End Driving

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end driving in this paper through the lens of environment augmentation. CAT aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios that are dynamically generated over time. A novel resampling technique is developed to turn log-replay real-world driving scenarios into safety-critical ones via probabilistic factorization, where the adversarial traffic generation is modeled as the multiplication of standard motion prediction sub-problems. Consequently, CAT can launch more efficient physical attacks compared to existing safety-critical scenario generation methods and yields a significantly less computational cost in the iterative learning pipeline. We incorporate CAT into the MetaDrive simulator and validate our approach on hundreds of driving scenarios imported from real-world driving datasets. Experimental results demonstrate that CAT can effectively generate adversarial scenarios countering the agent being trained. After training, the agent can achieve superior driving safety in both log-replay and safety-critical traffic scenarios on the held-out test set. Code and data are available at https://metadriverse.github.io/cat.Comment: 7th Conference on Robot Learning (CoRL 2023