One critical bottleneck that impedes the development and deployment of autonomous
vehicles is the prohibitively high economic and time costs required to validate their safety in …

To improve efficiency and reduce failures in autonomous vehicles, research has focused on
developing robust and safe learning methods that take into account disturbances in the …

As shown by recent studies, machine intelligence-enabled systems are vulnerable to test
cases resulting from either adversarial manipulation or natural distribution shifts. This has …

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated
negotiation skills with other road users when overtaking, giving way, merging, taking left and …

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 …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

Current deep learning based autonomous driving approaches yield impressive results also
leading to in-production deployment in certain controlled scenarios. One of the most popular …

Driving intelligence tests are critical to the development and deployment of autonomous
vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the …

Abstract Deep Neural Network-based systems are now the state-of-the-art in many robotics
tasks, but their application in safety-critical domains remains dangerous without formal …

This paper addresses the problem of evaluating learning systems in safety critical domains
such as autonomous driving, where failures can have catastrophic consequences. We focus …