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%.
By Chan-Bin Lim, Dong-Hee Paek, Seung-Hyun Kong
arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
By Runze Gan, Qing Li, Simon J. Godsill, Mike E. Davies, James R. Hopgood
arXiv:2608. 04201v1 Announce Type: new Abstract: State estimation for nonlinear dynamical systems is commonly performed with the Unscented Kalman filter (UKF), which propagates the state moments through deterministic sigma points and reports a posterior covariance at every step.
By Minhyeok Ko, Abdollah Shafieezadeh
arXiv:2606. 02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning.
By Jiho Lee, Nisar R. Ahmed, Rebecca Russell
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds.
arXiv:2607. 17351v1 Announce Type: new Abstract: DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model.
By Eli Goldenshluger, Barak Pinkovich, Chaim Baskin
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...
By Eric Xie, Daniil Lisus, Timothy D. Barfoot
arXiv:2609.01120v1 Announce Type: cross
Abstract: Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. T...
By Woong-Chan Byun, Seung-Hyun Kong
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.
By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
arXiv:2608. 10256v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism.
By Alexander Schi{\o}tz, Bertram Hage, Christian Rand, Felix Thomsen, Peder Heiselberg
arXiv:2606. 28396v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories.
By Emily Bejerano, Federico Tondolo, Devang Gupta, Aaron Mano Cherian, Taeyoo Kim, Ayaan Qayyum, Xiaofan Yu, Xiaofan Jiang
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.
By Kexin Tian, Sixu Li, Keshu Wu, Yang Zhou, Zhengzhong Tu