The ability of intelligent unmanned platforms to achieve autonomous navigation and
positioning in a large-scale environment has become increasingly demanding, in which …

Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …

The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …

We survey research on self-driving cars published in the literature focusing on autonomous
cars developed since the DARPA challenges, which are equipped with an autonomy system …

Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …

We demonstrate the first application of deep reinforcement learning to autonomous driving.
From randomly initialised parameters, our model is able to learn a policy for lane following in …

Perception system design is a vital step in the development of an autonomous vehicle (AV).
With the vast selection of available off-the-shelf schemes and seemingly endless options of …