Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

The development of autonomous vehicles (AVs) is becoming increasingly important as the
need for reliable and safe transportation grows. However, in order to achieve level 5 …

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …

Abstract Deep Reinforcement Learning (DRL) has demonstrated its strength in developing
intelligent systems. These systems shall be formally guaranteed to be trustworthy when …

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their
potential for many real-world tasks. However, vanilla RL and most safe RL approaches do …

Ensuring safe and capable motion planning is paramount for automated vehicles.
Traditional methods are limited in their ability to handle complex and unpredictable traffic …

Formally verifying deep reinforcement learning (DRL) systems suffers from both inaccurate
verification results and limited scalability. The major obstacle lies in the large overestimation …

Artificial Intelligence (AI) shows promising applications for the perception and planning tasks
in autonomous driving (AD) due to its superior performance compared to conventional …

Introduction: The escalating environmental crisis resulting from high carbon consumption
has led to severe consequences. Urgent measures to reduce carbon emissions are needed …

Ensuring safety of reinforcement learning (RL) algorithms is crucial to unlock their potential
for many real-world tasks. However, vanilla RL does not guarantee safety. In recent years …