11 accepted papers
This letter presents a technique that allows unmanned vehicles to escort a human to their destinations. Current human-centered following methods depend solely on human movement, which presents significant limitations. The complexity of human movement during tactical maneuvers can lead to erratic veh…
Freespace detection in unstructured off-road environments is critical for safe autonomous navigation but remains highly challenging due to ambiguous boundaries, diverse terrains, and long-tail safety-critical cases. Constructing large annotated datasets in such environments is prohibitively costly, …
Terrain classification is essential for traversability estimation and planning of unmanned ground vehicles (UGVs) in complex environments. Most existing approaches utilize fully supervised learning to classify terrains based on either exteroceptive or proprioceptive sensor modalities. However, visio
Visual Terrain Classification (VTC) plays a vital role in enabling unmanned ground vehicles to understand complex environments. Existing research relies on image-label pairs annotated by static label sets, where semantic ambiguity and high annotation costs constrain fine-grained terrain characteriza
Navigation through crowded intersections is a challenge for autonomous vehicles, where uncertainty arises from interaction with other road users, encountering new scenes and weathers, etc. Recent end-to-end autonomous control deep models learned from human drivers have shown promising driving perfor
Modern autonomous driving systems face substantial challenges when navigating dense intersections due to the high uncertainty introduced by other road users. Due to the complexity of the task, the autonomous vehicle needs to generate policies at multiple levels of abstraction. However, previous deep…
Deep neural networks using for real-world classification task require high reliability and robustness. However, the Softmax output by the last layer of network is often over-confident. We propose a novel confidence estimation method by considering model quality for deep classification models. Two me…
Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as a binary classification one, e.g. associating pixels with road or non-road labels. Whereas understanding scenes with fin…
This work addresses on the following problem: given a set of unsynchronized history observations of two scenes that are correlative on their dynamic changes, the purpose is to learn a cross-scene predictor, so that with the observation of one scene, a robot can onlinely predict the dynamic state of…
This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectorie…
In this paper, we propose a part-based particle filter for on-road vehicle tracking. The proposed model takes part-based strategies into account in a particle filter. By introducing a hidden state vehicle center position, vehicle parts particles can be updated efficiently as a whole sharing same mot…