In this paper, we propose an efficient vehicle trajectory prediction framework based on
recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different …

In this paper, we propose a deep learning based vehicle trajectory prediction technique
which can generate the future trajectory sequence of surrounding vehicles in real time. We …

Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision
with traffic participants. However, due to limited past information and the uncertainty in future …

As the number of various positioning sensors and location-based devices increase, a huge
amount of spatial and temporal information data is collected and accumulated. These data …

In order to drive safely and efficiently on public roads, autonomous vehicles will have to
understand the intentions of surrounding vehicles, and adapt their own behavior …

This paper addresses the problem of long term location prediction for collision avoidance in
Connected Vehicle (CV) environment where more information about the road and traffic data …

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 …

Automated vehicles and advanced driver-assistance systems require an accurate prediction
of future traffic scene states. The tendency in recent years has been to use deep learning …

This paper presents a surround vehicle motion prediction algorithm for multi-lane turn
intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network …

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to
achieve high-level driving safety in complex situations. However, most state-of-the-art …