Vehicle-to-everything (V2X) communication techniques enable the collaboration between
vehicles and many other entities in the neighboring environment, which could fundamentally …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

Driving maneuver decision-making is critical to the development and mass deployment of
automated vehicles (AVs). The prevailing approaches are stuck with either specific …

Accurately forecasting the future movements of surrounding vehicles is essential for safe
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …

Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …

We present a novel compact deep multi-task learning model to handle various autonomous
driving perception tasks in one forward pass. The model performs multiple views of semantic …

The safety and reliability of autonomous driving pivots on the accuracy of perception and
motion prediction pipelines, which reckons primarily on the sensors deployed onboard …

We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …

How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure
occupant safety. However, state-of-the-art methods using deep convolutional networks …