In automated driving, predicting and accommodating the uncertain future motion of other
traffic participants is challenging, especially in unstructured environments in which the high …

Autonomous vehicles face the challenge of providing safe transportation while efficiently
maneuvering in an uncertain environment. Considering surrounding vehicles, two types of …

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning
policies without active interactions, making it especially appealing for autonomous driving …

Autonomous driving requires efficient and safe decision making and motion planning in
dynamic and uncertain environments. Future movement of surrounding vehicles is often …

While automated research vehicles are already populating the roads, their commercial
availability at scale is still to come. Presumably, one of the key challenges is to derive …

For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is
imperative to assess the repercussions of its prospective actions by anticipating the …

In autonomous driving, the ego vehicle and its surrounding traffic environments always have
uncertainties like parameter and structural errors, behavior randomness of road users, etc …

Motion planning for car-like robots is one of the major challenges in automated driving. It
requires to solve a two-point boundary value problem that connects a start and a goal …

Deep neural network controllers for autonomous driving have recently benefited from
significant performance improvements, and have begun deployment in the real world. Prior …

This work presents a behavior planning algorithm for automated driving in urban
environments with an uncertain and dynamic nature. The algorithm allows to consider the …