DRVN is a regression testing tool that aims to diversify the test scenarios (road maps) to execute for testing and validating self-driving cars. DRVN harnesses the power of convolutional neural networks to identify possible failing roads in a set of generated examples before applying a greedy algorithm that selects and prioritizes the most diverse roads during regression testing. Initial testing discovered that DRVN performed well against random-based test selection.