Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …

Despite the rapid advancement of navigation algorithms, mobile robots often produce
anomalous behaviors that can lead to navigation failures. The ability to detect such …

Estimating the traversability of terrain should be reliable and accurate in diverse conditions
for autonomous driving in off-road environments. However, learning-based approaches …

In the rapidly evolving landscape of artificial intelligence, multimodal learning systems
(MMLS) have gained traction for their ability to process and integrate information from …

For the safe and successful navigation of autonomous vehicles in unstructured
environments, the traversability of terrain should vary based on the driving capabilities of the …

The issue of missing data in supervised learning has been largely overlooked, especially in
the deep learning community. We investigate strategies to adapt neural architectures for …

Shape measurements are crucial for evolutionary and developmental biology; however, they
present difficulties in the objective and automatic quantification of arbitrary shapes …

In autonomous driving, detection of abnormal driving behaviors is essential to ensure the
safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that …

Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to
humans, automated vehicles are supposed to perform anomaly detection. In this work, we …

We present a novel trajectory traversability estimation and planning algorithm for robot
navigation in complex outdoor environments. We incorporate multimodal sensory inputs …