arXiv AI
Jun 15
Q-Net: Queue Length Estimation via Kalman-based Neural Networks
arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing
arXiv:2401. 07468v4 Announce Type: replace-cross Abstract: The proposed model, CarSpeedNet, estimates scalar vehicle speed from a window of three-axis smartphone acceleration, without gyroscope, wheel-odometry, vehicle-bus, or positioning input at inference.
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arXiv Machine Learning
Jul 8
Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
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.
arXiv Machine Learning
Jun 25
Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
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.
arXiv AI
4d ago
Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking
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.
arXiv AI
Jun 24
AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
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.
arXiv Machine Learning
Jul 13
Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification
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.
arXiv AI
Jul 8
Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors
arXiv:2607. 05663v1 Announce Type: cross Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments.
arXiv Machine Learning
Aug 11
Data collection from highways: a geometric, class-agnostic approach to embedded vehicle counting
arXiv:2608. 07643v1 Announce Type: cross Abstract: Traffic data collection is dominated today by deep object detectors followed by tracking-by-detection, a pipeline that presupposes what is often missing in practice: a detector already trained on the class one wants to count.
arXiv Computer Vision
Sep 25
Smartphone-Based Method for Automated Speed Enforcement
The paper presents a smartphone-based system that uses computer vision to automatically estimate vehicle speed and identify vehicles by license plate, make/model, and color. Experiments on a Brazilian dataset and real-world recordings in Austin, Texas show moderate recognition rates: 46% for license plates, 60.8% for color, 48.6% for make, and 16.89% for make/model. The study also discusses legal, technological, and practical considerations for deploying such smartphone recordings in traffic enforcement.
arXiv Machine Learning
Sep 18
QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles
The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
arXiv AI
Aug 19
MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure
MotoSafety is an edge-AI system designed to assess collision risk for two‑wheeler riders under varying time pressure. It is trained on a large dataset of 129,000 labeled time‑series sequences from 153 simulator rides, capturing 64 features related to vehicle dynamics, control inputs, proximity, and behavioral violations. The model achieves 94.97% accuracy and 99.33% ROC AUC, with only 1.15 M parameters and 0.135 ms latency, making it suitable for low‑cost CPU deployment and demonstrating strong transferability to other domains.
arXiv Machine Learning
Sep 10
An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception
arXiv:2609.05516v1 Announce Type: cross Abstract: Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single...