The automated real-time recognition of unexpected situations plays a crucial role in the
safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This …

While recent developments in autonomous vehicle (AV) technology highlight substantial
progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto …

In company with the data explosion over the past decade, deep neural network (DNN)
based software has experienced unprecedented leap and is becoming the key driving force …

ComOpT is an open-source research tool for coverage-driven testing of autonomous driving
systems, focusing on planning and control. Starting with (i) a meta-model characterizing …

The substantial resource usage required to create ample scenarios for testing Autonomous
Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on …

Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an
effective way to evaluate the robustness of autonomous driving systems. However, the …

Abstract We present AutonoVi-Sim, a novel high-fidelity simulation platform for autonomous
driving data generation and driving strategy testing. AutonoVi-Sim is a collection of high …

Offline model-level testing of autonomous driving software is much cheaper, faster, and
diversified than in-field, online system-level testing. Hence, researchers have compared …

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle
managers, eg, government or industrial companies, still need a way to tell these self-driving …