Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and
revolutionize how people travel and how we transport goods. Many of the major challenges …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications as …

Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …

Vehicle autonomy has the potential to bring many social benefits, such as improved traffic
safety and increased productivity. Modern autonomous vehicles are able to sense their local …

The growing adoption of autonomous vehicles (AVs) holds the promise of transforming
transportation systems, enhancing traffic safety, and supporting environmental sustainability …

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

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

Autonomous navigation in crowded, complex urban environments requires interacting with
other agents on the road. A common solution to this problem is to use a prediction model to …

To safely and successfully navigate complex and dense driving scenes, automated vehicles
need to develop situational awareness, which requires understanding the current behavior …

The ability to interact with other road participants is essential for autonomous vehicles in
order to navigate under highly complex or critical driving scenarios. It is an extremely …