Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

Microscopic traffic simulation plays a crucial role in transportation engineering by providing
insights into individual vehicle behavior and overall traffic flow. However creating a realistic …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

We view intersection handling on autonomous vehicles as a reinforcement learning
problem, and study its behavior in a transfer learning setting. We show that a network trained …

Reactive and safe agent modellings are important for nowadays traffic simulator designs
and safe planning applications. In this work, we proposed a reactive agent model which can …

Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL
models typically involves in a multi-step process: pre-training RL models on simulators …

Efficient understanding of the environment is a crucial prerequisite for autonomous driving,
but explicitly modeling the environment is hard to come true. In contrast, imitation learning, in …

The field of autonomous driving has seen increasing proposed use of machine learning
methodologies. However, there are still challenges in applying such methods since …