Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Deployed into an open world, object detectors are prone to open-set errors, false positive
detections of object classes not present in the training dataset. We propose GMM-Det, a real …

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 …

Perception in automated vehicles (AV) is the main factor in achieving safe driving. In this
perception task, multi-object detection (MOD) in diverse driving situations is the main …

Besides standard cameras, autonomous vehicles typically include multiple additional
sensors, such as lidars and radars, which help acquire richer information for perceiving the …

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately
capture the uncertainties of the perception system, especially knowing the unknown …

3D pedestrian detection is a challenging task in automated driving because pedestrians are
relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR …

This paper presents an novel object type classification method for automotive applications
which uses deep learning with radar reflections. The method provides object class …

This paper evaluates the ability of some state-of-the-art Machine Learning models, namely
SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to …

With the recent advancements in machine learning technology, the accuracy of autonomous
driving object detection models has significantly improved. However, due to the complexity …