This paper presents novel hybrid architectures that combine grid-and point-based
processing to improve the detection performance and orientation estimation of radar-based …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and
radars in different combinations for 3D object detection. Specialized feature extractors take …

Road safety for automated vehicles requires accurate and early detection of stationary
objects in the vehicle's path. Radar can use doppler to effectively identify stationary objects …

Many radar signal processing methodologies are being developed for critical road safety
perception tasks. Unfortu-nately, these signal processing algorithms are often poorly suited …

Automotive radar sensors provide valuable information for advanced driving assistance
systems (ADAS). Radars can reliably estimate the distance to an object and the relative …

As the automotive industry evolves, visual perception systems to provide awareness of
surroundings to autonomous vehicles have become vital. Conventional deep neural …

Group regression is commonly used in 3D object detection to predict box parameters of
similar classes in a joint head, aiming to benefit from similarities while separating highly …

Deploying radar object detection models on resource-constrained edge devices like the
Raspberry Pi poses significant challenges due to the large size of the model and the limited …

Radar plays a big role in performing perception tasks for Autonomous Driving in adverse
weather—such as during rainy, foggy, hazy, and snowy days. In the current era of rising …