Behavior and motion planning play an important role in automated driving. Traditionally,
behavior planners instruct local motion planners with predefined behaviors. Due to the high …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

Achieving a proper balance between planning quality, safety and efficiency is a major
challenge for autonomous driving. Optimisation-based motion planners are capable of …

In this paper, we introduce the first published planner to drive a car in dense, urban traffic
using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set …

In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …

In this paper, we introduce the first learning-based planner to drive a car in dense, urban
traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a …

The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …

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 …

We present an approach for learning spatial traversability maps for driving in complex, urban
environments based on an extensive dataset demonstrating the driving behaviour of human …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …