30 accepted papers
Graph outlier detection is a critical task for identifying rare, deviant patterns in graph-structured data. However, prevalent methods based on graph convolution are fundamentally challenged by the ''Homophily Trap'': the aggregation of features from neighboring nodes inadvertently contaminates the…
Hybrid planner switching framework (HPSF) for autonomous driving needs to reconcile high-speed driving efficiency with safe maneuvering in dense traffic. Existing HPSF methods often fail to make reliable mode transitions or sustain efficient driving in congested environments, owing to heuristic scen…
Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and
Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which suffer from low computational efficiency and accuracy. The proposed SSF-PAN can…
Novel view image synthesis for large-scale outdoor traffic scenes presents significant challenges, including inaccurate depth measurements, moving objects, wide-angle rendering requirements, and the increased demand for memory and computational resources. In this paper, we propose an adaptive pipeli
Compared with real-time multi-object tracking (MOT), offline multi-object tracking (OMOT) has the advantages to perform 2D-3D detection fusion, erroneous link correction, and full track optimization but has to deal with the challenges from bounding box misalignment and track evaluation, editing, and
Temporal Knowledge Graphs (TKGs) incorporate the temporal feature to express the transience of knowledge by describing when facts occur. TKG extrapolation aims to infer possible future facts based on known history, which has garnered significant attention in recent years. Some existing methods treat…
Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which suffer from low computational efficiency and accuracy. The proposed SSF-PAN can
Robust and accurate perception and prediction of the driving scenarios are crucial for autonomous driving vehicles (ADV). State-of-the-art ADV frameworks have evolved from conventional modular design to an end-to-end (E2E) pipeline that enables joint feature learning and optimization. However, the e
The scene flow estimation aims at accurately achieving the motion of 3D points, imposing challenges like mis-registration, object occlusions, and non-uniform upsampling. This paper introduces a scene flow estimation framework featuring a unified scene flow estimator, a symmetric cost volume approach
Simulation data can be accurately labeled and have been expected to improve the performance of data-driven algorithms, including object detection. However, due to the various domain inconsistencies from simulation to reality (sim-to-real), cross-domain object detection algorithms usually suffer from…
Virtual reality (VR) is a promising data engine for autonomous driving (AD). However, data fidelity in this paradigm is often degraded by VR inconsistency, for which the existing VR approaches become ineffective, as they ignore the inter-dependency between low-level VR synchronizer designs (i.e., da
Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this letter, we prese
To ensure real-time response to passengers, existing solutions to the vehicle dispatch problem typically optimize dispatch policies using small batch windows and ignore the spatial-temporal dynamics over the long-term horizon. In this paper, we focus on improving the long-term performance of ride-sh…
This paper considers active SLAM problem for 3D deformable environments where the trajectory of the robot is planned to optimize the SLAM results. A planning strategy combining an efficient global planner with an accurate local planner is proposed to solve the problem. Simulation results under diffe…
The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i.e., AEMCARL)…
2D&3D object detection always suffers from a dramatic performance drop when transferring the model trained in the source domain to the target domain due to various domain shifts. In this paper, we propose a Joint Self-Training (JST) framework to improve 2D image and 3D point cloud detectors with ali…
Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ
The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (R
Providing safety guarantees for Autonomous Vehicle (AV) systems with machine-learning based controllers remains a challenging issue. In this work, we propose Simplex-Drive, a framework that can achieve runtime safety assurance for machine-learning enabled controllers of AVs. The proposed Simplex-Dri…
In colonoscopy procedures, it is important to rebuild and visualize the colonic surface to minimize the missing regions and reinspect for abnormalities. Due to the fast camera motion and deformation of the colon in standard forward-viewing colonoscopies, traditional simultaneous localization and map…
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of o…
Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability const…
Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for auton…
Shared autonomous mobility-on-demand systems hold great promise for improving the efficiency of urban transportation, but are challenging to implement due to the huge scheduling search space and highly dynamic nature of requests. This paper presents a novel optimal schedule pool (OSP) assignment app…
Full body scanning plays an important role in automated industrial manufacture and inspection. It requires the fusion of multi-view point cloud data and consumes large computational resources when the geometry of corresponding point clouds are unknown. Structured Light Illumination (SLI) is one of t…
On-demand ride-sharing is a promising way to improve mobility efficiency and reliability. The quality of passenger experience and the profit achieved by these platforms are strongly affected by the vehicle dispatch policy. However, existing ride-sharing research seldom considers travel time uncertai…
The challenges of developing low-cost, large-scale multi-robot navigation systems include noisy measurements, a large number of robots, and computing efficiency for collision avoidance. This paper presents a distributed motion planning framework for a large number of robots to navigate with robust c…
The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRN…
Simulation environments play a centric role in the research of sensor fusion and robot control. This paper presents Pavilion, a novel open-source simulation system, for robot perception and kinematic control based on the Unreal Engine and the Robot Operating System (ROS). The novelty of this work in…