In autonomous driving, complex traffic scenarios can cause situations that require human
supervision to resolve safely. Instead of only reacting to such events, it is desirable to predict …

Autonomous driving is of great interest in both research and industry. The high cost has
been one of the major roadblocks that slow down the development and adoption of …

With the continuous maturation and application of autonomous driving technology, a
systematic examination of open-source autonomous driving datasets becomes instrumental …

In the last 5 years, with the vast improvements in computing technologies, eg, sensors,
computer vision, machine learning, and hardware acceleration, and the wide deployment of …

End-to-end autonomous driving has witnessed remarkable progress. However, the
extensive deployment of autonomous vehicles has yet to be realized, primarily due to 1) …

End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …

Autonomous driving is a prominent topic in the fields of artificial intelligence and machine
learning, with several studies being undertaken in order to bring driverless vehicles to the …

This paper introduces a novel trajectory prediction approach for autonomous vehicles (AVs),
adeptly addressing the challenges of missing observations and the need for adherence to …

The aspiration of the next generation's autonomous driving (AD) technology relies on the
dedicated integration and interaction among intelligent perception, prediction, planning, and …