Robotic vehicles (RVs) are being adopted in a variety of application domains. Despite their
increasing deployment, many security issues with RVs have emerged, limiting their wider …

Ensuring the safety of self-driving cars is important, but neither industry nor authorities have
settled on a standard way to test them. Deploying self-driving cars for testing in regular traffic …

In this paper, we outline the use of a genetic algorithm for test parameter optimization in the
context of autonomous and automated driving. Our approach iteratively optimizes test …

Determining possible failure scenarios is a critical step in the evaluation of autonomous
vehicle systems. Real world vehicle testing is commonly employed for autonomous vehicle …

As self-driving systems become better, simulating scenarios where the autonomy stack may
fail becomes more important. Traditionally, those scenarios are generated for a few scenes …

This paper presents a method for testing the decision making systems of autonomous
vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

Autonomous vehicle (AV) software is typically composed of a pipeline of individual
components, linking sensor inputs to motor outputs. Erroneous component outputs …

Assuring the safety of self-driving cars and other fully autonomous vehicles presents
significant challenges to traditional software safety standards both in terms of content and …

Evaluating and improving planning for autonomous vehicles requires scalable generation of
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …