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 …

Autonomous vehicles rely on their perception systems to acquire information about their
immediate surroundings. It is necessary to detect the presence of other vehicles …

Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV).
With the help of deep learning and big data, it is possible to understand the between-vehicle …

In a mixed traffic environment of human and autonomous driving, it is crucial for an
autonomous vehicle to predict the lane change intentions and trajectories of vehicles that …

Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …

This paper presents a novel vehicle motion forecasting method based on multi-head
attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi …

As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …

Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …

Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …