Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a
formidable challenge, especially in mixed autonomy environments. Traditional approaches …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …

Nowadays, autonomous driving cars have become commercially available. However, the
safety of a self-driving car is still a challenging problem that has not been well studied …

As autonomous driving technology progresses, the need for precise trajectory prediction
models becomes paramount. This paper introduces an innovative model that infuses …

Self-driving or autonomous vehicles need to efficiently and continuously navigate in
complex traffic environments by analyzing the surrounding scene, understanding the …

To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …