arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
By Ting Gao, Elvin Isufi, Winnie Daamen, Erik-Sander Smits, Serge Hoogendoorn
arXiv:2607. 05669v1 Announce Type: cross Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction.
By Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch
arXiv:2605. 07412v2 Announce Type: replace Abstract: Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons.
By Feng Liu (Beijing Jiaotong University), Kejia Li (Beijing Jiaotong University), Zhiwei Yang (DiDi Company), Chunwei Yang (DiDi Company), Qun Li (DiDi Company), Guobin Wu (DiDi Company), Qiang Ni (Lancaster University), Ruipeng Gao (Beijing Jiaotong University)
The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.
By Ruizheng Xu (Heudiasyc), Lounis Adouane (Heudiasyc), Javier Iba\~nez-Guzm\'an, Cl\'ement Zinoune
arXiv:2603. 13343v3 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnostic signals and are validated on simulated or industrial benchmark data.
By Kushal Khemani (Independent Researcher, India), Anjum Nazir Qureshi (Rajiv Gandhi College of Engineering Research,Technology)
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
By Ofir Kruzel, Itzik Klien