No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Obtaining accurate and diverse human motion prediction is essential to many industrial
applications, especially robotics and autonomous driving. Recent research has explored …

Understanding and forecasting future trajectories of agents are critical for behavior analysis,
robot navigation, autonomous cars, and other related applications. Previous methods mostly …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …

Accurate and reliable prediction of future motions of the nearby agents and effective
environment understanding will contribute to high-quality and meticulous path planning for …

In complex and dynamic urban traffic scenarios, the accurate trajectory prediction of
surrounding pedestrians with interactive behaviors plays a vital role in the self-driving …

Advances in representation learning have led to great success in understanding and
generating data in various domains. However, in modeling vector graphics data, the pure …

Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes
has become more and more significant in many intelligent systems and applications. The …