Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

Motion planning of automated vehicles requires dynamical models to ensure that obtained
trajectories are drivable. An often overlooked aspect is that motion planning is usually done …

The classical method of autonomous racing uses real-time localisation to follow a
precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) …

Detailed road maps are an important building block for autonomous driving. They accelerate
creating a semantic environment model within the vehicle and serve as a backup solution …

Collision mitigation and collision avoidance systems in intelligent vehicles reduce the
severity and number of accidents. To determine the optimal point in time at which such …

Autonomous vehicles must comply with traffic rules. However, most motion planners do not
explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned …

How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of
an Automated Driving System (ADS) equipped vehicle? Is a more significant number of …

Local path planning is a key task for the motion planners of autonomous vehicles since it
commands the vehicle across its environment while avoiding any obstacles. To perform this …

Motion planners for autonomous vehicles must obtain feasible trajectories in real-time
regardless of the complexity of traffic conditions. Planning approaches that discretize the …

Reachability analysis is a formal method to guarantee safety of dynamical systems under the
influence of uncertainties. A substantial bottleneck of all reachability algorithms is the …