Single and multi-objective test cases prioritization for self-driving cars in virtual environments

Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment. However, these tests are very expensive and are too many to be run frequently within limited time constraints. In this paper, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer, prioritizes virtual tests for SDCs according to static features of the roads we designed to be used within the driving scenarios. These features can be collected without running the tests, which means that they do not require past execution results. We introduce two evolutionary approaches to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. These two approaches, called SO-SDC-Prioritizer and MO-SDC-Prioritizer, use single-objective and multi objective genetic algorithms, respectively, to find trade-offs between executing the less expensive tests and the most diverse test cases earlier. Our empirical study conducted in the SDC domain shows that MO-SDC-Prioritizer significantly (p-value<= 0.1 − 10) improves the ability to detect safety-critical failures at the same level of execution time compared to baselines: random and greedy-based test case orderings. Besides, our study indicates that multi-objective meta-heuristics outperform single-objective approaches when prioritizing simulation-based tests for SDCs. MO-SDC-Prioritizer prioritizes test cases with a large improvement in fault detection while its overhead (up to 0.45% of the test execution cost) is negligible

A General Model For Soft Body Simulation In Motion

Soft bodies are the models in which the bodies deform during animated frames depending on the interaction between themselves and environment. This paper presents a parametric general model for soft body simulation in which structure, deformation, and volume controls generate animated deformations restricted by a set of constraints within or without an environment of gravitation. In this model, the soft body shape is controlled by structure control and anamorphosis of the soft body is created by deformation control, while the mass is approximated by volume control. A set of constraints for these controls further restrict the types of deformation of the soft body. By selecting specific methods for structure, deformation, and volume controls with a set of constraints, we demonstrate a variety of appealing fluid-like surfaces and respiration of lungs for validating the usefulness of the general model. © 2011 IEEE