Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

Reinforcement learning (RL) has shown superior performance in solving sequential
decision problems. In recent years, RL is gradually being used to solve unmanned driving …

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine
learning for classification and regression problems. It relies on vectorized input data …

An autonomous vehicle would benefit from being able to predict trajectories of other vehicles
in its vicinity for improved safety. In order for the self-driving car to plan safe trajectories …

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its
applications include but are not limited to autonomous driving, robot navigation, and …

Traffic prediction is a keystone for building smart cities in the new era and has found wide
applications in traffic scheduling and management, environment policy making, public …

Trajectory prediction modules are key enablers for safe and efficient planning of
autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently …

A Brain-computer interface (BCI) system establishes a novel communication channel
between the human brain and a computer. Most event related potential-based BCI …

Recent years have witnessed the growing popularity of connectionist temporal classification
(CTC) and attention mechanism in scene text recognition (STR). CTC-based methods …

Trajectory prediction plays a key role in the decision-making system of autonomous
vehicles. The existing trajectory prediction models have the problem of accuracy …