The design of high-level decision-making systems is a topical problem in the field of
autonomous driving. In this paper, we combine traditional rule-based strategies and …

While machine-learning-based methods suffer from a lack of transparency, rule-based (RB)
methods dominate safety-critical systems. Yet the RB approaches cannot compete with the …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

Autonomous Driving requires high levels of coordination and collaboration between agents.
Achieving effective coordination in multi-agent systems is a difficult task that remains largely …

Autonomous driving has gained significant advancements in recent years. However,
obtaining a robust control policy for driving remains challenging as it requires training data …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

Designing reliable decision strategies for autonomous urban driving is challenging.
Reinforcement learning (RL) has been used to automatically derive suitable behavior in …

Reinforcement learning (RL) is a powerful data-driven control method that has been largely
explored in autonomous driving tasks. However, conventional RL approaches learn control …

In many real-world decision making problems, reaching an optimal decision requires taking
into account a variable number of objects around the agent. Autonomous driving is a domain …