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 …

With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …

In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …

In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …

This paper presents online-capable deep learning model for probabilistic vehicle trajectory
prediction. We propose a simple encoder-decoder architecture based on multihead …

Path planning for autonomous vehicles in dynamic environments is an important but
challenging problem, due to the constraints of vehicle dynamics and existence of …

Autonomous vehicles (AVs) need to reason about the multimodal behavior of neighboring
agents while planning their own motion. Many existing trajectory planners seek a single …

We propose a data-driven control framework for autonomous driving which combines
learning-based risk assessment with personalized, safety-focused, predictive control …