The future of autonomous vehicles lies in the convergence of human-centric design and
advanced AI capabilities. Autonomous vehicles of the future will not only transport …

Multiple studies have illustrated the potential for dramatic societal, environmental and
economic benefits from significant penetration of autonomous driving. However, all the …

We present Pylot, a platform for autonomous vehicle (AV) research and development, built
with the goal to allow researchers to study the effects of the latency and accuracy of their …

Digital twin, an emerging representation of cyberphysical systems, has attracted increasing
attentions very recently. It opens the way to real-time monitoring and synchronization of real …

Autonomous vehicle (AV) stacks are typically built in a modular fashion, with explicit
components performing detection, tracking, prediction, planning, control, etc. While …

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and
potential challenges for existing modes of transportation. Currently, driving assistance …

Today, and possibly for a long time to come, the full driving task is too complex an activity to
be fully formalized as a sensing-acting robotics system that can be explicitly solved through …

The capability to learn and adapt to changes in the driving environment is crucial for
developing autonomous driving systems that are scalable beyond geo-fenced operational …

Autonomous vehicles (AVs) must drive across a variety of challenging environments that
impose continuously-varying deadlines and runtime-accuracy tradeoffs on their software …

With the development and the increasing interests in ML/DL fields, companies are eager to
apply Machine Learning/Deep Learning approaches to increase service quality and …