In recent years, precision agriculture has been introducing groundbreaking innovations in the field, with a strong focus on automation. However, research studies in robotics and autonomous navigation often rely on controlled simulations or isolated field trials. The absence of a realistic common benchmark represents a significant limitation for the diffusion of robust autonomous systems under real complex agricultural conditions. Vineyards pose significant challenges due to their dynamic nature, and they are increasingly drawing attention from both academic and industrial stakeholders interested in automation. In this context, we introduce the TEMPO-VINE dataset, a large-scale multi-temporal dataset specifically designed for evaluating sensor fusion, simultaneous localization and mapping (SLAM), and place recognition techniques within operational vineyard environments. TEMPO-VINE is the first multi-modal public dataset that brings together data from heterogeneous LiDARs of different price levels, AHRS, RTK-GPS, and cameras in real trellis and pergola vineyards, with multiple rows exceeding 100 m in length. In this work, we address a critical gap in the landscape of agricultural datasets by providing researchers with a comprehensive data collection and ground truth trajectories in different seasons, vegetation growth stages, terrain and weather conditions. The sequence paths with multiple runs and revisits will foster the development of sensor fusion, localization, mapping and place recognition solutions for agricultural fields. The dataset, the processing tools and the benchmarking results will be available at the dedicated webpage upon acceptance.

We call (1) the process model and (2) the measurement model.

Thank you for your encouraging comments. Thank you for your thorough and helpful review. We appreciate all of your feedback. This is explained in the "sensor selection" paragraph at the end of the paper and We are glad that you understand and appreciate the significance of Theorem 2. Empirical results/better demonstrations It also suggests that with the "right" constraints put in place, a nonlinear method should do very well. For example, we can try multiple process models on the flu data.

Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on precise temporal synchronization exposes it to new vulnerabilities. In this paper, we introduce DejaVu, an attack that exploits the in-vehicular network and induces delays across sensor streams to create subtle temporal misalignments, severely degrading downstream MMF-based perception tasks. Our comprehensive attack analysis across different models and datasets reveals the sensors' task-specific imbalanced sensitivities: object detection is overly dependent on LiDAR inputs, while object tracking is highly reliant on the camera inputs. Consequently, with a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%. We further demonstrated two attack scenarios using an automotive Ethernet testbed for hardware-in-the-loop validation and the Autoware stack for end-to-end AD simulation, demonstrating the feasibility of the DejaVu attack and its severe impact, such as collisions and phantom braking.

INFRASTRUCTURE SENSOR-ENABLED VEHICLE DA T A GENERA TION USING MUL TI-SENSOR FUSION FOR PROACTIVE SAFETY APPLICA TIONS A T WORK ZONE Suhala Rabab Saba Department of Civil, Construction & Environmental Engineering, The University of Alabama Smart Communities and Innovation Building (SCIB), 28 Kirkbride Lane, Tuscaloosa, AL 35487-0288 Email: ssaba@crimson.ua.edu Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 3 ABSTRACT Infrastructure-based sensing and real-time trajectory generation hold significant promise for improving safety in high-risk roadway segments like work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by (i) integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and (ii) employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70% compared to individual sensors while preserving lateral accuracy within 1-3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments. Keywords: work zone, fusion, lidar, camera, localization, safety Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 4 INTRODUCTION Work zone crashes do not necessarily impact only the vehicles and people directly involved; instead, they have cascading effects that cause operational delays for passing vehicles and project completion delays for work zone contractors. The Federal Motor Carrier Safety Administration (FMCSA) report indicates that commercial motor vehicles (CMVs) are involved in one-third of work zone fatal crashes, although they represent only 5% of all vehicular traffic (1). In addition, speed is a contributing factor in 26% of all fatal work zone crashes (2). According to Jiao (2022) (3), 13% of CMV drivers are fatigued when they are involved in crashes.

Gesture-based control for mobile manipulators faces persistent challenges in reliability, efficiency, and intuitiveness. This paper presents a dual-hand gesture interface that integrates TinyML, spectral analysis, and sensor fusion within a ROS framework to address these limitations. The system uses left-hand tilt and finger flexion, captured using accelerometer and flex sensors, for mobile base navigation, while right-hand IMU signals are processed through spectral analysis and classified by a lightweight neural network. This pipeline enables TinyML-based gesture recognition to control a 7-DOF Kinova Gen3 manipulator. By supporting simultaneous navigation and manipulation, the framework improves efficiency and coordination compared to sequential methods. Key contributions include a bimanual control architecture, real-time low-power gesture recognition, robust multimodal sensor fusion, and a scalable ROS-based implementation. The proposed approach advances Human-Robot Interaction (HRI) for industrial automation, assistive robotics, and hazardous environments, offering a cost-effective, open-source solution with strong potential for real-world deployment and further optimization.

We call (1) the process model and (2) the measurement model.

Thank you for your encouraging comments. Thank you for your thorough and helpful review. We appreciate all of your feedback. This is explained in the "sensor selection" paragraph at the end of the paper and We are glad that you understand and appreciate the significance of Theorem 2. Empirical results/better demonstrations It also suggests that with the "right" constraints put in place, a nonlinear method should do very well. For example, we can try multiple process models on the flu data.

Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propose a novel architecture based on Quantum Artificial Intelligence by enabling quantum and AI at various levels of navigation decision making and communication process in Autonomous vehicles : Quantum Neural Networks for multimodal sensor fusion, Nav-Q for Quantum reinforcement learning for navigation policy optimization and finally post-quantum cryptographic protocols for secure communication. Quantum neural networks uses quantum amplitude encoding to fuse data from various sensors like LiDAR, radar, camera, GPS and weather etc., This approach gives a unified quantum state representation between heterogeneous sensor modalities. Nav-Q module processes the fused quantum states through variational quantum circuits to learn optimal navigation policies under swift dynamic and complex conditions. Finally, post quantum cryptographic protocols are used to secure communication channels for both within vehicle communication and V2X (Vehicle to Everything) communications and thus secures the autonomous vehicle communication from both classical and quantum security threats. Thus, the proposed framework addresses fundamental challenges in autonomous vehicles navigation by providing quantum performance and future proof security. Index Terms Quantum Computing, Autonomous Vehicles, Sensor Fusion

Fast contact detection is crucial for safe human-robot collaboration. Observers based on proprioceptive information can be used for contact detection but have first-order error dynamics, which results in delays. Sensor fusion based on inertial measurement units (IMUs) consisting of accelerometers and gyroscopes is advantageous for reducing delays. The acceleration estimation enables the direct calculation of external forces. For serial robots, the installation of multiple accelerometers and gyroscopes is required for dynamics modeling since the joint coordinates are the minimal coordinates. Alternatively, parallel robots (PRs) offer the potential to use only one IMU on the end-effector platform, which already presents the minimal coordinates of the PR. This work introduces a sensor-fusion method for contact detection using encoders and only one low-cost, consumer-grade IMU for a PR. The end-effector accelerations are estimated by an extended Kalman filter and incorporated into the dynamics to calculate external forces. In real-world experiments with a planar PR, we demonstrate that this approach reduces the detection duration by up to 50% compared to a momentum observer and enables the collision and clamping detection within 3-39ms.