Over the last few years, we have witnessed tremendous progress on many subtasks of
autonomous driving including perception, motion forecasting, and motion planning …

We present a visual localization framework based on novel deep attention aware features
for autonomous driving that achieves centimeter level localization accuracy. Conventional …

Accurate localization is a foundational capacity, required for autonomous vehicles to
accomplish other tasks such as navigation or path planning. It is a common practice for …

Accurate localization ability is fundamental in autonomous driving. Traditional visual
localization frameworks approach the semantic map-matching problem with geometric …

Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we
have seen a lot of sensor-rich vehicles (eg Robo-taxi) driving on the street autonomously …

Robust and accurate localization is an essential component for robotic navigation and
autonomous driving. The use of cameras for localization with high definition map (HD Map) …

Camera localization from monocular images has been a long-standing problem, but its
robustness in dynamic environments is still not adequately addressed. Compared with …

We address the problem of vehicle selflocalization from multi-modal sensor information and
a reference map. The map is generated off-line by extracting landmarks from the vehicle's …

Semantic understanding and localization are fundamental enablers of robot autonomy that
have been tackled as disjoint problems for the most part. While deep learning has enabled …

Electric vhicles and autonomous driving dominate current research efforts in the automotive
sector. The two topics go hand in hand in terms of enabling safer and more environmentally …