Vehicular edge computing (VEC) is a promising technology for supporting computation-
intensive vehicular applications with low latency at the network edges. Vehicles offload their …

The wide application of edge cloud computing in the Internet of Vehicles (IoV) provides
lower latency, more efficient computing power, and more reliable data transmission services …

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic
monitoring and driving assistant, deep learning-based object detection (DL-OD) has been …

The advancement of Internet of Vehicles has brought out various vehicular applications, and
some of the applications are computation-intensive or delay-sensitive. Vehicular fog …

The existing approaches to design efficient safety-critical control applications is constrained
by limited in-vehicle sensing and computational capabilities. In the context of automated …

Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we
present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and …

Collaborative object localization and sharing at smart intersections promises to improve
situational awareness of traffic participants in key areas where hazards exist due to visual …

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role
in understanding and enhancing the environmental perception of autonomous vehicles …

The field of autonomous driving research has made significant strides toward achieving full
automation, endowing vehicles with self-awareness and independent decision making …

In the Vehicle to Vehicle (V2V) communication based on Optical Camera Communication
(OCC), optical signals are transmitted using LED arrays and received employing cameras. In …