6 accepted papers
Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain interactions pose significant challenges such as traction loss and dynamic instability. Despite recent progress in learning-based traversability predicti
This paper presents a unified prediction and planning algorithm for an autonomous vehicle to interact with an uncertain human-driven vehicle. Predicting human motion is challenging due to inherent uncertainties in diverse human internal states, i.e., driving styles and rationality. To address these…
Autonomous racing confronts significant challenges in safely overtaking Opponent Vehicles (OVs) that exhibit uncertain trajectories, stemming from unknown driving policies. To address these challenges, this study proposes heterogeneous kernel metrics for Deep Kernel Learning (DKL), designed to robus
LiDAR-based Simultaneous Localization and Mapping (SLAM) encounters a substantial challenge in the form of accumulating errors, which can adversely impact its reliability. Loop closing techniques have been extensively employed to counteract this issue. Nonetheless, the loop closing conundrum remains
This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicle-terrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to…