Predicting the motion of surrounding vehicles is an important function of autonomous
vehicles. However, most of the current state-of-the-art trajectory prediction models rely …

This article proposes an interaction and safety-aware motion-planning method for an
autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates …

Accurate prediction of agent motion trajectories is crucial for autonomous driving,
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …

Multi-agent collaborative navigation is prevalent in modern transportation systems, including
delivery logistics, warehouse automation, and personalised tourism, where multiple moving …

The ability to predict the future trajectories of traffic participants is crucial for the safe and
efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model …

To improve forecasting accuracy and capture intricate interactions within transportation
networks, information fusion approaches are crucial for traffic predictions based on graph …

The availability of high-quality datasets is crucial for the development of behavior prediction
algorithms in autonomous vehicles. This paper highlights the need for standardizing the use …

Accurate velocity prediction is a practically crucial yet challenging task for intelligent
vehicles. The challenge derives from the time-varying vehicle states, and the dynamic …

Traditional highway safety and monitoring solutions, reliant on surveillance cameras, face
limitations due to their dependence on high-speed internet connectivity and the remote …

To safely and successfully navigate complex and dense driving scenes, automated vehicles
need to develop situational awareness, which requires understanding the current behavior …