The paper introduces PR-IMM, a data‑driven state predictor‑based Interacting Multiple Model tracking method that integrates a transformer‑based prediction model with radar Doppler measurements. PR-IMM combines this prediction model with conventional CV, CA, and CT motion models, dynamically weighting their prior positions according to mode probabilities. Experiments demonstrate that PR-IMM reduces position‑estimation error by 57.3% compared to the standard IMM and by 16.5% compared to the prediction model alone, while also lowering ID switches by 25.3% and improving IDF1 by 9.6%.
arXiv:2607. 13573v1 Announce Type: cross Abstract: Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch.
The paper introduces a Motion Dynamics Kalman Filter (MD‑KF) for 3D multi‑object tracking that adapts to motion variation while keeping a single‑model structure. It improves state estimation by scaling motion components to reduce drift, especially for distant or occluded objects. Experiments on KITTI, Waymo Open Dataset, and real vehicle tests show MD‑KF matches multimodel KF performance with a 1.72× speedup.
arXiv:2609.21000v1 Announce Type: cross Abstract: Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustne...
arXiv:2606. 29861v1 Announce Type: cross Abstract: Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics.
arXiv:2607. 05705v1 Announce Type: cross Abstract: Multi-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles.
The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.
This paper presents Transformer and Performer models for forecasting surface vessel trajectories in confined waters using AIS position data. It introduces positional heat maps generated via Kernel Density Estimation and encoded with a CNN to incorporate local vessel movement patterns. The models, trained with quantile loss, achieve superior performance over prior work and provide prediction intervals essential for collision avoidance.
The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.
The paper introduces a multi‑modal late‑fusion perception pipeline for object detection and tracking in autonomous racing. It combines independent detections from cameras, LiDARs, and RADARs to produce timely and robust state estimates of surrounding vehicles. The tracking framework compensates for detection delays and incorporates vehicle dynamics and track layout knowledge, and its effectiveness is confirmed through real‑world experiments in diverse critical scenarios.
arXiv:2606. 25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers before a collision becomes unavoidable.
arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
arXiv:2609.22857v1 Announce Type: new Abstract: Dynamic object segmentation and ego-motion estimation are closely coupled problems in autonomous driving, as accurate ego-motion estimation typically r...