The driving behavior of autonomous vehicles has a significant impact on safety for all traffic
participants. Unlike current traffic participants, autonomous vehicles in the future will also …

Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel
efficiency and reduce congestion. They represent the main trend in future intelligent …

To learn a reward function that a driver adheres to is of importance to the human-like design
of autonomous driving systems. Inverse reinforcement learning (IRL) is one of the recent …

Many autonomous driving motion planners generate trajectories by optimizing a reward/cost
functional. Designing and tuning a high-performance reward/cost functional for Level-4 …

One of the key factors to ensure the safe operation of autonomous and semi-autonomous
vehicles in dynamic environments is the ability to accurately predict the motion of the …

Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …

The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety
systems that aim to identify critical events such as near-crash situations and traffic violations …

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 …

Collision-free movement of an autonomous vehicle using reinforcement learning | Proceedings
of the 10th European conference on Artificial intelligence skip to main content ACM Digital …

Reinforcement learning with safety constraints is promising for autonomous vehicles, of
which various failures may result in disastrous losses. In general, a safe policy is trained by …