This paper overviews challenges in perception and decision making for intelligent, or highly
automated, automotive vehicles. We illustrate our development of a complete perception …

The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

Although planning is a crucial component of the autonomous driving stack, researchers
have yet to develop robust planning algorithms that are capable of safely handling the …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …

Planning an optimal route in a complex environment requires efficient reasoning about the
surrounding scene. While human drivers prioritize important objects and ignore details not …

In this paper we propose a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …

In highly interactive driving scenarios, the actions of one agent greatly influence those of its
neighbors. Planning safe motions for autonomous vehicles (AVs) in such interactive …

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