10 accepted papers
Trajectory prediction plays a significant role in autonomous driving, with current challenges primarily focused on capturing complex interactions in traffic scenes. Previous methods usually directly encode non-interactive and interactive information together, and then decode them for trajectory pred…
End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However the oversized neural networks render them impractical for deployment on resource-constrained systems which unavoidably requires more computational time and re…
The objective of pedestrian trajectory prediction is to estimate the future paths of pedestrians by leveraging historical observations, which plays a vital role in ensuring the safety of self-driving vehicles and navigation robots. Previous works usually rely on a sufficient amount of observation ti…
Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regre…
Path planning in real road traffic refers to the navigation of an autonomous vehicle through an obstacle-filled environment. It is crucial for the comfort, safety, and efficiency of the autonomous driving experience. It is advantageous to plan paths in the reference line-based Frenet frames rather t
The Frenét frame is commonly used in motion planning for its superiority of reshaping nonconvex curving boundaries and decoupling lateral and longitudinal behaviors. Nevertheless, such frame is not friendly for describing vehicle-related characteristics such as body shape and classical dynamic model
Motion prediction of multiple agents in a dynamic scene is a crucial component in many real applications, including intelligent monitoring and autonomous driving. Due to the complex interactions among the agents and their interactions with the surrounding scene, accurate trajectory prediction is sti…
Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning a collision-free and appropriate path for the autonomous robots, especially in crowded scenes. However, it is a particularly challenging task because there are complex and subtle interactions among ped
Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representati…
Pedestrian trajectory prediction is crucial for many important applications. This problem is a great challenge because of complicated interactions among pedestrians. Previous methods model only the pairwise interactions between pedestrians, which not only oversimplifies the interactions among pedest…