Diverse transport demands have resulted in the wide existence of heterogeneous vehicle
automation systems. While these systems have demonstrated effectiveness, they also pose …

Prototyping and validating hardware–software components, sub-systems and systems within
the intelligent transportation system-of-systems framework requires a modular yet flexible …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

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 …

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 …

As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …

The emerging development of connected and automated vehicles imposes a significant
challenge on current vehicle control and transportation systems. This paper proposes a …

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

Advances in Single-vehicle intelligence of automated driving has encountered great
challenges because of limited capabilities in perception and interaction with complex traffic …