Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to
operate safely. Small, far-away, or highly occluded objects are particularly challenging …

Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based
detectors have achieved impressive performance, the high cost of LiDAR sensors precludes …

Current 3D object detectors for autonomous driving are almost entirely trained on human-
annotated data. Although of high quality, the generation of such data is laborious and costly …

Detecting dynamic objects and predicting static road information such as drivable areas and
ground heights are crucial for safe autonomous driving. Previous works studied each …

Autonomous driving datasets are often skewed and in particular, lack training data for
objects at farther distances from the ego vehicle. The imbalance of data causes a …

An autonomous driving system requires a 3D object detector, which must perceive all
present road agents reliably to navigate an environment safely. However, real-world driving …

In this paper, we present LaserNet, a computationally efficient method for 3D object
detection from LiDAR data for autonomous driving. The efficiency results from processing …

Current perception models in autonomous driving have become notorious for greatly relying
on a mass of annotated data to cover unseen cases and address the long-tail problem. On …

Modern perception systems in the field of autonomous driving rely on 3D data analysis.
LiDAR sensors are frequently used to acquire such data due to their increased resilience to …

LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly,
although autonomous vehicles (AVs) must detect both near-field objects (for collision …