Browse Topic: Navigation and guidance systems

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Quantifying the Accuracy of Detecting Lateral Movement Onset for a Selection of Bicycle GPS Computers2026-01-0531To be published on 04/07/2026
Bicycle computers and apps record, at minimum, positional data over time, and this data is commonly used in accident reconstruction to inform the behavior of the bicycle and rider prior to an incident in question. This positional data is primarily calculated using the Global Positioning System (GPS) and thus specific accuracy and precision is not well quantified. To improve this understanding when it comes to relative accuracy of lateral movements, a series of controlled tests were performed and recorded with several commercially available bicycle GPS computers and apps and compared against a RaceLogic VBox 3i ADAS with Real-Time Kinematic corrections from a RaceLogic Base Station. The testing consisted of 5 sets of 15 runs: 15 runs riding down the center of a lane, 15 making a 6 ft lateral left movement, 15 making 12 ft lateral left movements, 15 making 6 ft lateral right movements, and 15 making 12 ft lateral right movements. Each test run was ended with hard braking to a stop to
Sweet, David Michael, Bretting, Gerald, O'Brien, Nathan, Wilhelm, Christopher
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-0045To be published on 04/07/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-0148To be published on 04/07/2026
Offroad autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6x4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6x4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6x4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational
Dekhterman, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-0044To be published on 04/07/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse reward settings. Furthermore, a reactive avoidance reward function based on velocity obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
LiDAR-Driven A* Path Planning with Dynamic Obstacle Avoidance for Autonomous Navigation in ROS2026-01-0030To be published on 04/07/2026
Autonomous mobile robots are becoming a key part of everyday operations in industries like manufacturing, logistics, healthcare, and even home assistance. A core requirement for these robots is the ability to navigate efficiently and reliably within their operating environments. To do this automation, the robot needs to understand its surroundings, figure out where it is on a map, and find a safe path from where it is to where it needs to go without bumping into anything. This paper presents an effective grid-based path planning solution for autonomous indoor navigation with a mobile robot. Achieving reliable and collision-free navigation in changing environments is a major challenge for mobile robotics. This is especially true when obstacles can appear unexpectedly, requiring quick re-planning. To tackle this issue, an improved A* algorithm was implemented to work closely with LiDAR for environmental awareness. The improved algorithm was added to the robot’s navigation system, and
Devaraj, Sriram Sanjeev, Park, Jungme
Development of technology to distinguish contaminants using LiDAR near-field point cloud based on real-field data2026-01-0020To be published on 04/07/2026
Sensing technologies are essential to autonomous driving, enabling environmental perception and supporting navigation and its decision-making. However, most publicly available datasets are collected under perfect weather conditions and fail to capture the challenges of real-world environments. To address this gap, we built a large-scale dataset from extensive driving campaigns across diverse regions of the United States, covering conditions such as rain, snow, dust, clear weather and anti-ice material affects. Data were acquired through a multimodal sensing devices - including perception cameras, LiDAR, infrared sensors, and external monitoring systems - providing a comprehensive representation of the vehicle's operational environment. Building on this dataset, our study focuses on LiDAR data, emphasizing contaminant classification as a critical requirement for sensor-cleaning strategies essential to reliable autonomous driving. Using raw LiDAR signals collected over 22,000km of
KIM, HUNJAE
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To be published on 04/07/2026
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Bias and uncertainty in the time, position, and speed data of consumer-grade GPS devices2026-01-0544To be published on 04/07/2026
The goal of this study is to quantify the accuracy and uncertainty of position and speed measurements acquired by a wide range of consumer-grade GPS-enabled devices while road cycling. We acquired position and speed data simultaneously from 13 consumer devices (e.g., phones, watches, bicycle computers) at their maximum sampling rate (typically 1 Hz) during 10 hours (thirty 20 minute sessions) and 150 km of road cycling in a suburban environment. The position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system and the speed data from these devices were compared to speed data acquired with high-resolution wheel rotation sensors (23,040 pulses per revolution) synchronized with the RTK data. Based on these comparisons, we generated probability distributions for the errors in the position and speed measured by each of the consumer devices. This study provides foundational data needed to quantify the accuracy and uncertainty across
Booth, Gabrielle R., Mitchell, Alan L., Siegmund, Gunter P.
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-0113To be published on 02/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Multi-Storey AI Based Parking System2026-28-0129To be published on 02/12/2026
This paper presents a comprehensive design and implementation of an AI-based parking management system tailored to address the challenges of modern parking facilities. The system is designed to enhance efficiency, reduce manual intervention, and optimize operational costs while providing a seamless experience for drivers. The core architecture of the system integrates advanced technologies, including computer vision and IR sensors, thereby minimizing reliance on conventional sensors and streamlining the parking process. By leveraging computer vision, the system continuously monitors parking areas in real-time, accurately detecting vehicle occupancy status, and dynamically updating the availability of parking spaces. This real-time monitoring capability enables efficient space utilization, allowing for the optimization of parking resources. In addition to optimizing space allocation, the proposed system prioritizes security through the integration of video surveillance. Utilizing AI
N, Kalaiarasi, Gupta, Shivansh, Hajarnis, Mihir, Anand, Vikas
MINER’S SAFETY BOT2026-28-0116To be published on 02/12/2026
Mining remains one of the most hazardous industries, with workers constantly exposed to life-threatening conditions such as toxic gases, high temperatures, and confined spaces. To address these concerns, we propose the design and development of an autonomous Miner’s Safety Bot aimed at enhancing underground safety through real-time environmental monitoring and intelligent navigation. The system is built around the ESP32 microcontroller, integrating critical sensors including the MQ7 gas sensor for detecting carbon monoxide and methane, the DHT11 sensor for measuring ambient temperature, and an ultrasonic sensor for obstacle detection. These components work to monitor hazardous parameters and assist the bot in navigating through narrow mine shafts. A servo motor connected to a remote-controlled chassis adjusts the bot's direction when obstacles are detected. The bot employs the ESP32-CAM module to transmit live visuals from the mining site to external observers. Furthermore, sensor data
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
Algorithm Development for AI Driven Path Planning Using Augmented Reality for Navigating in Indian Complex Road Scenarios2026-26-00381/16/2026
The road infrastructure in India has complex navigational challenges with most of the road unstructured especially in rural areas. Decision-making becomes a challenge for drivers in unpredictable environments such as narrow roads, flooded roads and heavy traffic. In this paper, an Augmented Reality based ML-Algorithm for Driver Assistance (ARMADA) has been proposed that improves awareness to safely maneuver in these conditions. The methodology for development and validation of this Augmented Reality (AR) based algorithm contains multiple steps. Firstly, extensive data collection is conducted using real time recording and benchmark datasets like Berkeley Deep Drive (BDD) and Indian Driving Dataset (IDD). Secondly, collected data are annotated and trained using an optimal machine learning (ML) model to accurately identify the complex scenario. In third step, an ARMADA algorithm is developed, integrating these models to estimate road widths, detect floods and provide seamless driver
Anandaraj, Prem Raj, Sivakumar, Vishnu, Thanikachalam, Ganesh, L, Radhakrishnan, Motoki, Yaginuma, Selvam, Dinesh Kumar
Bio-Inspired Decentralized Mapping for Autonomous Vehicles2026-26-06351/16/2026
The BioMap system represents a groundbreaking approach to collaborative mapping for autonomous vehicles, drawing inspiration from ant colony behavior and swarm intelligence. It implements a fully decentralized protocol where vehicles use virtual pheromone trails to mark areas of uncertainty, change, or importance, enabling efficient map consensus without centralized coordination. Key innovations include novel pheromone-based compression algorithms and bio-inspired consensus mechanisms that allow real-time adaptation to dynamic environments. In a simulated urban scenario (Town10HD), three vehicles achieved balanced load distribution (±1.8% variance) and comprehensive coverage of a 253.2m × 217.9m × 22.4m area. The final fused map contained 311 chunks with 72,785 particles and required only 10.4 MB of storage. Approximately 49.2% of map particles exceeded the pheromone significance threshold, indicating active importance marking, while no high-uncertainty regions remained. These results
Bhargav, Anirudh S, Subbarao, Chitrashree
Proactive AI-Enhanced Cybersecurity for Next-Gen Automotive Infotainment ECU2026-26-06191/16/2026
With the rapid advancement of connected vehicle technologies, infotainment Electronic Control Units (ECUs) have become central to user interaction and connectivity within modern vehicles. However, this enhanced functionality has introduced new vulnerabilities to cyberattacks. This paper explores the application of Artificial Intelligence (AI) in enhancing the cybersecurity framework of infotainment ECUs. The study introduces AI-powered modules for threat detection and response, presents an integrated architecture, and validates performance through simulation using MATLAB, CANoe, and NS-3. This approach addresses real-time intrusion detection, anomaly analysis, and voice command security. Key benefits include zero-day exploit resistance, scalability, and continuous protection via OTA updates. The paper references real-world automotive cyberattack cases such as OTA vulnerability patches, Connected Drive exploits, and Uconnect hack, emphasizing the critical need for AI-enabled proactive
More, Shweta, Kulkarni, Shraddha, Kumar, Priyanshu, Ghanwat, Hemant, Joshi, Vivek
Path Planning and Control Strategies for a Semi-Autonomous Light Commercial Vehicle Navigating and Avoiding Static Obstacles in Urban Environments2026-26-01341/16/2026
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring
Ayyappan, Vimal Raj, Dhanopia, Rashmi, Ali, Ashpak, N, Ragesh, Sato, Hiromitsu
Map-Less Yet Accurate: Trajectory Prediction for Traffic Agents Using Online HD Map Reconstruction for Autonomous Driving2026-26-00391/16/2026
Accurate trajectory prediction of traffic agents is critical for enabling safer and more reliable autonomous driving, particularly in urban driving scenarios where close-range interactions are most safety critical. High-definition (HD) and standard-definition (SD) maps play a vital role in this process by providing lane topology and directional cues for forecasting agent movements. However, HD maps are expensive and resource-intensive to create, often requiring specialized sensors, while SD maps lack the precision needed for reliable autonomous navigation. To address this, we propose a novel framework for trajectory prediction that leverages online reconstruction of HD maps using vehicle-mounted cameras, offering a scalable and cost-effective alternative. Our method achieves improvements in predicting accuracy, particularly in close-range scenarios, the most crucial for urban driving, while also performing robustly in settings without pre-built maps. Furthermore, we introduce a new
Upreti, Minali, Girijal, Rahul, B A, NaveenKumar, Thontepu, Phani, Ghosh, Shankhanil, Chakraborty, Bodhisattwa
Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework2026-26-01351/16/2026
Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments for applications like automated parking and low speed autonomous driving. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths are widely used and can generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce smoothed curve paths appropriate for real-world driving. This method utilizes the practical approach of the original path, but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, improved energy efficiency, less actuator wear, and improved high-speed tracking. This paper
S, Shriniyathi, A, Josana, Edwin J, Joel, T, Akshayaa, M, Senthil Vel, Kumar, Vimal
Comparative Analysis and Implementation of Data Streaming Methodologies for Vehicle Navigation Mode for Electric Trucks2026-26-06921/16/2026
As electric trucks become more central to modern logistics, the need for smarter, more adaptive route planning is growing rapidly. This paper presents a key navigation feature for analyzing and recalibrating such optimized routes in real time. Integrating map features into the navigation mode improves user experience by offering real-time navigation and dynamic route adjustments based on traffic updates, road closures, vehicle coordinates and deviation in expected energy consumption. This study compares the performance of Server sent events (SSE), web sockets, and Application programming interface (API) polling methodologies, focusing on metrics such as data transmission efficiency, latency, resource utilization, scalability, and reliability. Our results demonstrate the advantages and limitations of each method, providing insights into their suitability for real-time route optimization in electric truck logistics. The results highlight the potential of SSE in achieving efficient and
Bhandari, Mehul, Kaur, Prabhjot, Dadoo, Vishal, Mahendrakar, Shrinidhi, Ramanaiah, Rachala
Identification of Productive and Non-Productive Activities in Agricultural Machinery Using ECU and GPS Parameters through Machine Learning2026-26-01021/16/2026
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with
Maharana, Devi prasad, Gangsar, Purushottam, gokhale, Varun, Pandey, Anand Kumar
Validation of 0D-1D Co-Simulation Strategies in Cold Conditions for ‘Full EV Digital Vehicle’2026-26-04481/16/2026
Electric Vehicles (EV) are embedded with increased software algorithms coupled with several physical systems. It demands the efficacy of components which are linked together to build a system. The digital models reviewed in this paper are at system-level and full vehicle-level, comprising many components and control design, analysis, and optimization. Systems pertaining to each functionality such as, A/C (Air Conditioning) loop, E-Powertrain (Electric Powertrain), HEVC (Hybrid Electric Vehicle Controller), Cooling system, Battery Management System (BMS), Vehicle control system etc. together make an ‘Integrated Digital Vehicle.’ Fidelity of Intersystem co-simulation [AMESIM + SIMULINK] is key to validating thermal and energy strategies. This paper elucidates the correlation of Digital Vehicle compared to Test for Thermal Strategy in different driving scenarios and Energy management. Validation of Digital vehicle with 52kWh, 40kWh High Voltage Battery for Intercity Travel of Customer
Sarapalli Ramachandran, Raghuveeran, Srinivasan, Rangarajan, Saravanan, Vivek, Dutta, Souham, Pichon, Martin, Leclerc, Cedric, Guemene, Alexis-Scott
Risk-Aware Navigation Framework for Autonomous and Human-Driven Vehicles: Integrating Crash Probability Data for Safer Mobility12-09-03-00191/9/2026
This article presents a system to incorporate crash risk into navigation routing algorithms, enabling safety-aware path optimization for autonomous and human-driven vehicles alike. Current navigation systems optimize travel time or distance, while our approach adds crash probability as a routing criterion, allowing users to balance efficiency with safety. We transform disparate data sources, including traffic counts, crash reports, and road network data, into standardized risk metrics. Because traffic volume data only exist for a small subset of road segments, we develop a solution to project average daily traffic estimates to an entire road inventory using machine learning, achieving sufficient coverage for practical implementation. The framework computes exposure-normalized crash rates weighted by severity and integrates these metrics into routing cost functions compatible with existing navigation algorithms. The key strength of our solution is its scalability. In addition to the
Skaug, Lars, Nojoumian, Mehrdad
Peering through Lunar Darkness: Laser Invention to Steer the Next Generation of Moon LandersTBMG-544551/1/2026
Advanced Navigation Sydney, Australia
Deep Learning-Based Improvement and Validation of Vehicles’ Speed Extraction from Aerial Videos2025-01-734912/31/2025
Unmanned Aerial Vehicles (UAVs) offer high efficiency, low cost, and strong mobility, making them well-suited for traffic vehicle detection. However, dense targets, rapid scene changes, and small object sizes in aerial videos reduce detection accuracy, which in turn affects the precision of speed extraction algorithms. To address these issues, this paper proposes a speed extraction method that integrates an improved You Only Look Once Version 11 (YOLOv11) with the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm. On the detection side, several architectural enhancements are introduced. A Haar wavelet-based HWD downsampling module preserves fine-grained details, a CSK2_m multi-scale convolution block with a CCFM feature fusion structure strengthens cross-scale representation, and an additional detection head at the P2 layer improves the recall of tiny objects in complex scenes. Extensive experiments on a hybrid dataset constructed from VisDrone2019 and a custom UAV dataset
Ye, Xin, Cheng, Xiaoxuan, Li, Xiangdong
Comparison of Parameters and States Estimators for the Longitudinal Dynamics of a Military Heavy Vehicle2025-36-012612/18/2025
Technological innovations in military vehicles are essential for enhancing efficiency, safety, and operational capability in complex scenarios. Advances such as navigation system automation and the introduction of autonomous vehicles have transformed military mobility. State estimators enable the precise monitoring of critical variables that are not directly accessible by sensors, providing real-time information to controllers and improving dynamic response under variable conditions. Their integration is crucial for the development of advanced control systems. This study aims to develop and compare parameter and states estimators for military heavy vehicles using three methodologies: particle filter, extended Kalman filter, and moving horizon state estimation. Computational simulations employ Pacejka’s magic formula to model tire behavior, and the vehicle modeling is based on a simplified quarter-car model, with an emphasis on longitudinal dynamics. In the end, the estimators are
Barros, Leandro Silva, Sousa, Daniel Henrique Braz, Rodrigues, Gustavo Simão, Lopes, Elias Dias Rossi
Ship Encounter Situation Recognition Based on Transformer2025-99-031512/17/2025
Intelligent ships represent a crucial trend in the development of the maritime industry and will become the predominant vessel form in the future. Intelligent ships must have efficient perception and situational analysis capabilities in complex navigation environments to achieve intelligent decision-making and safe navigation. Maritime traffic safety is a critical issue for the global shipping industry, and maritime situational awareness is essential for ensuring safe navigation in waterways. This paper addresses the problem of intelligent identification of potential navigational risks in ship navigation environments and proposes a Transformer-based approach for ship encounter situation recognition. This method utilizes Automatic Identification System (AIS) data to extract encounter features. Contextual Position Encoding and Coordinate Attention mechanisms are introduced into the model to capture spatial correlation and directional features, enhancing the accuracy of determining
Ma, Tongyue, Pan, Mingyang, Li, Shaoxi, Hu, Jingfeng, Li, Chao
Risk Assessment and Identification of Freight Vehicle Trip Chains2025-99-042112/10/2025
Although the number of trucks is low, their accident rate is high, and the consequences of accidents are severe. This paper is based on GPS data from 100 trucks, with each trip chain defined by a vehicle’s stay time greater than 20 minutes. The kinematic parameters for each trip chain are then extracted, and the entropy weight method is used to calculate the weights of various parameters. A random forest model is applied to select 11 key indicators, including speed and acceleration. The entropy weight-TOPSIS algorithm is used to assess the risk of each trip chain for the trucks. Different combinations of continuous and discontinuous trip chain scenarios are constructed. Finally, support vector machines (SVM) and decision tree methods are used for risk prediction under different trip chain combinations. The results show that the 11 selected key indicators provide an accuracy of 95.74% for describing the sample. In general, the SVM model shows better prediction accuracy than the decision
Huang, Yunhe, Xiong, Zhihua, Li, Jiayu
3D LiDAR for Improved Rover Traversal and ImageryTBMG-5429512/1/2025
Planetary and lunar rover exploration missions can encounter environments that do not allow for navigation by typical, stereo camera-based systems. Stereo cameras meet difficulties in areas with low ambient light (even when lit by floodlights), direct sunlight, or washed-out environments. Improved sensors are required for safe and successful rover mobility in harsh conditions. NASA Goddard Space Flight Center has developed a Space Qualified Rover LiDAR (SQRLi) system that will improve rover sensing capabilities in a small, lightweight package. The new SQRLi package is developed to survive the hazardous space environment and provide valuable image data during planetary and lunar rover exploration.
A New Method to Protect Aircraft Against GPS OutagesTBMG-5429612/1/2025
Dangling from a weather balloon 80,000 feet above New Mexico, a pair of antennas sticks out from a Styrofoam cooler. From that height, the blackness of space presses against Earth’s blue skies. But the antennas are not captivated by the breathtaking view. Instead, they listen for signals that could make air travel safer.
Big Data Trajectory Analysis and Traffic Flow Framework Construction for Ship Manipulation Decision-Making2025-99-013411/11/2025
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
Kong, Xiangyu, Shao, Guoyu
Software Radio Based ASM Signal Ranging Delay Measurement System2025-99-012111/11/2025
In contemporary society, where Global Navigation Satellite Systems (GNSS) are utilised extensively, their inherent fragility gives rise to potential hazards with respect to the safety of ship navigation. In order to address this issue, the present study focuses on an ASM signal delay measurement system based on software defined radio peripherals. The system comprises two distinct components: a transmitting end and a receiving end. At the transmitting end, a signal generator, a first time-frequency synchronisation device, and a VHF transmitting antenna are employed to transmit ASM signals comprising dual Barker 13 code training sequences. At the receiving end, signals are received via software-defined radio equipment, a second time-frequency synchronisation device, a computing host, and a VHF receiving antenna. Utilising sliding correlation algorithms enables accurate time delay estimation. The present study leverages the high performance and low cost advantages of the universal
Li, Hao, Sun, Xiaowen, Wang, Tianqi, Zhou, Zeliang, Wang, Xiaoye
Research on Off-Road Performance Optimization Strategy for Agricultural Smart Vehicles Based on Traction Control System2025-99-011811/11/2025
(TC)The paper presents a designed and evaluated optimal traction control (TC) strategy for unmanned agriculture vehicle, where onboard sensors acquire various real-time information about wheel speed, load sharing, and terrain characteristics to achieve the precise control of the powertrain by establishing an optimal control command; moreover, the developed AMT-adaptive SMC combines the AMT adaptive control algorithm and the SMC to implement the dynamic gear shifting, torque output, and driving mode switching to obtain an optimal power distribution according to different speed demand and harvest load. Based on the establishment of models of the autonomous agriculture vehicle and corresponding tire model, a MATLAB/Simulink method based on dynamic simulation is adopted to simulate the unmanned agricultural vehicle traversing different terrains conditions. The results from comparison show that the energy saving reaches 19.0%, rising from 2. 1 kWh/km to 1. 7 kWh/km, an increase in
Feng, Zhenghao, Lu, Yunfan, Gao, Duan, An, Yi, Zhou, Chuanbo
Machine Learning Based off-Road Vehicle Turn Identification Using Vehicle & GPS Parameters2025-28-034411/6/2025
Identification of different types of turns during field operation of off-road vehicles is critical in the overall vehicle development as it is helpful in identifying & optimizing machine performance, correct duty cycle, fuel economy, stability analysis, accurate path planning, customer usage pattern & designing the critical components, etc. In this study, a machine learning (ML) based methodology has been developed to detect the off-road vehicle turns using vehicle & GPS parameters. Three most common types of off-road vehicles turn conditions e.g., Straight line, Bulb turn, and Three-Point turn have been considered. Different vehicle parameters (like latitude & longitude, compass bearing, yaw rate, vehicle speed, swash plate angle, engine speed, percent load at vehicle speed, raise lower front & PTO channels) generated during field test have been used here. These vehicle parameters are further processed, analysed and used in ML learning model building. Four ML models e.g., SVM, K-NN
Rai, Rohit, Gangsar, Purushottam
AI/ML Based Audio Classification and Transcription for Automotive and Off-Highway Systems2025-28-032311/6/2025
Modern automotive systems generate a wide range of audio-based signals, such as indicator chimes, turn signals, infotainment system audio, navigation prompts, and warning alerts, to facilitate communication between the vehicle and its occupants. Accurate Classification and transcription of this audio is important for refining driver aid systems, safety features, and infotainment automation. This paper introduces an AI/ML-powered technique for audio classification and transcription in automotive environments. The proposed solution employs a hybrid deep learning architecture that leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs), trained using labeled audio samples. Moreover, an Automatic Speech Recognition (ASR) model is integrated for transcribing spoken navigation prompts and commands from infotainment systems. The proposed system delivers reliable results in real-time audio classification and transcription, facilitating better automation and
Singh, Shwetha, Kamble, Amit, Mohanty, Ananta, Kalidas, Sateesh
Development of Anti-SideSlip Control for Motorcycles Using IMU2025-32-003711/3/2025
In motorcycle racing and other competitions, there is a technique to intentionally slide the rear wheel to make turns more quickly. While this technique is effective for high-speed riding, it is difficult to execute and carries risks such as falling. Therefore, an anti-sideslip control system that suppresses unintended or excessive sideslip is needed to ensure safe, natural, and smooth turning. In anti-sideslip control, the slip angle is usually used as a control parameter. However, for motorcycles, it is necessary to know the absolute direction of the vehicle's movement. To determine this, GPS or optical sensors are required, but using such sensors for driving is costly and may not provide accurate measurements due to contamination or other environmental factors, making it impractical. Therefore, an anti-sideslip control system was developed by calculating another parameter that indicates the characteristics of the slip angle, without measuring the slip angle itself, thus eliminating
Nakano, Kyosuke, Kawai, Kazunori, Takeuchi, Michinori
Waterway Traffic Safety Evaluation Based on the Factor Analysis Method2025-99-005910/17/2025
With the continuous advancement of economic globalization, international trade has been developing rapidly, and Marine transportation is an important part of international trade. Shipping and port are facing greater opportunities for development, the shipping industry gradually to the modernization, specialization, large-scale rapid development. The rapid increase of the number of ships, the tonnage of ships, the flow of ships, the density of ships, and the busier of the channel traffic, these changes make the navigable conditions of navigable waters. Under the limited port water conditions, the ship traffic density increases, the ship distress probability increases, and the navigation environment becomes more complex, which make the water transportation safety face greater challenges and threats. The waters of dense traffic flow, danger, accidents, the importance of its navigation safety assessment is self-evident. Preventing some accident risk in the process of ship navigation has
You, Haowei, Zheng, Zhongyi
Finite Element-Based Study on the Frangibility of Airport Navigation Light2025-99-006210/17/2025
This study establishes models of airport vertical navigation lights and aircraft vulnerable components (wings and landing gear) using SOLIDWORKS. Based on the frangibility standards for airport navigation facilities, the control dimensions of the circular tube model for navigation lights are determined. Numerical simulations are conducted in ANSYS Workbench to analyze collisions between aircraft wings/landing gear and navigation lights under three different velocity conditions. Internal energy analysis, bidirectional force response, and stress nephograms during the impact process are evaluated. The results indicate that current standards ensure that collisions with vertical navigation lights during takeoff and landing do not cause deformation or damage to aircraft vulnerable components, thereby guaranteeing the safety of aircraft and pilots.
Wang, Jianwu, Song, Xiaobo, Wei, Yan, Liu, Hongwei, You, Shengnan, Sun, Jinkun
Hydrodynamic and Flow Distribution Responses to Integrated River Regulation Measures in the Yangtze River’s Chizhou Reach2025-99-004010/17/2025
River regulation engineering is pivotal for harmonizing flood resilience, ecological integrity, and navigation efficiency in large alluvial systems, particularly under intensified hydrological stressors. The Yangtze River, Asia’s largest fluvial network, has experienced altered hydro-sedimentary regimes and exacerbated channel instability due to cascade reservoir operations, demanding adaptive strategies to stabilize dynamic reaches. This study investigates hydrodynamic and flow distribution responses to integrated regulation measures in the Chizhou Reach—a vulnerable alluvial segment characterized by severe bank erosion, sedimentation-induced flow imbalances, and constrained floodplains. Using a 1:500/1:100 scaled hydraulic model validated under flood and low-flow conditions, we assess synergistic effects of dredging, submerged dams, and flow-regulating groynes. Here we show that dredging the Wanchuanzhou right branch increases its flow diversion ratio by 1.71% (annual average flow
Gao, Jin, Feng, Lilei, Ruan, Junsheng, Lu, Lixin, Yan, Jun
Research on collision Avoidance Path Planning of Inland Waterway Vessels Based on Deep Learning Technology2025-99-001510/17/2025
How to realize the intelligent collision avoidance of inland waterway ships has become a hot issue in the field of transportation. The navigation status, position information and speed of inland vessels can be obtained by using the shipborne Beidou terminal and AIS, so as to realize the real-time monitoring of the ship’s operation status and the real-time optimization of collision avoidance path planning. In the process of track classification and prediction, it is necessary to use deep learning algorithms to train and learn historical track data, so as to generate a model that can accurately predict future tracks, and make collision avoidance path planning decisions on this basis, so as to realize the intelligence of water traffic organization and ship collision avoidance.
Liu, Xingchen, Cui, Jianzhang, Kong, Lingqi
Target Geolocation Method Based on Monocular Vision2025-99-002110/17/2025
In the intelligent traffic system (ITS), roadside sensing can obtain the movement status of various objects in the traffic scene in real time with a globalized perspective, which is of great significance for traffic flow optimization, accident early warning, and rescue afterwards. Accurate target positioning is one of the key links to realize these functions, which can not only help the traffic management department to grasp the traffic condition in time, but also provide the basis for rescue personnel to respond quickly when an accident occurs, so as to minimize the damage caused by the accident. Therefore, a method for acquiring the Global Positioning System (GPS) coordinates of objects relying on monocular surveillance installed on the roadside is proposed in this paper. By combining the target detection algorithm and the coordinate transformation method, and considering the information such as the installation status and internal parameters of the camera, the pixel positions of
Zhang, Nijia, Lu, Mingfeng, Chen, Ziyi, Zhang, Feng, Tao, Ran, Hu, Weidong, Fu, Xiongjun
Utilizing Acoustic Signals for Autonomous Swarm Formation in Micro-RobotsTBMG-5388910/1/2025
Animals like bats, whales, and insects have long used acoustic signals for communication and navigation. Now, an international team of scientists have taken a page from nature’s playbook to model micro-sized robots that use sound waves to coordinate into large swarms that exhibit intelligent-like behavior. The robot groups could one day carry out complex tasks like exploring disaster zones, cleaning up pollution, or performing medical treatments from inside the body, according to team lead Igor Aronson, Huck Chair Professor of Biomedical Engineering, Chemistry, and Mathematics at Penn State.
Monitoring and Control of Each Nanosatellite within a Cluster of NanosatellitesTBMG-5389210/1/2025
NASA has developed an innovative combination of a Magnetometer, low-powered ElectroMagnets, and Resonant Inductive Coupling (MEMRIC) to create and control relative positioning of nano satellites within a cluster. This is a game-changing approach to enable distributed nanosatellite (nanosat) clusters. The focus is on low-cost propulsion, navigation, and power sharing. Each of these functions can share the same basic technology.
Simulating Off-Road Autonomous Navigation: Fast Simulation of Vegetation Override2025-01-04789/16/2025
Navigation in off-road terrains is a well-studied problem for self-driving and autonomous vehicles. Frequently cited concerns include features like soft soil, rough terrain, and steep slopes. In this paper, we present the important but less studied aspect of negotiating vegetation in off-road terrain. Using recent field measurements, we develop a fast running model for the resistance on a ground vehicle overriding both small vegetation like grass and larger vegetation like bamboo and trees. We implement of our override model into a 3D simulation environment, the MSU Autonomous Vehicle Simulator (MAVS), and demonstrate how this model can be incorporated into real-time simulation of autonomous ground vehicles (AGV) operating in off-road terrain. Finally, we show how this model can be used to simulate autonomous navigation through a variety of vegetation with a PID speed controller and measuring the effect of navigation through vegetation on the vehicle speed.
Goodin, Christopher, Moore, Marc N., Hudson, Christopher R., Carruth, Daniel W., Salmon, Ethan, Cole, Michael P., Jayakumar, Paramsothy, English, Brittney
VORA™: Vision for Off-Road Autonomy2025-01-04419/16/2025
The Vision for Off-road Autonomy (VORA) project used passive, vision-only sensors to generate a dense, robust world model for use in off-road navigation. The research resulted in vision-based algorithms applicable to defense and surveillance autonomy, intelligent agricultural applications, and planetary exploration. Passive perception for world modeling enables stealth operation (since lidars can alert observers) and does not require more expensive or specialized sensors (e.g., radar or lidar). Over the course of this three-phase program, SwRI built components of a vision-only navigation pipeline and tested the result on a vehicle platform in an off-road environment.
Towler, Meera Day, Garza, Harold A., Chambers, David R.
Simulating Off-Road Autonomous Navigation: Fast Simulation of Vegetation Override2025-01-04529/16/2025
Navigation in off-road terrains is a well-studied problem for self-driving and autonomous vehicles. Frequently cited concerns include features like soft soil, rough terrain, and steep slopes. In this paper, we present the important but less studied aspect of negotiating vegetation in off-road terrain. Using recent field measurements, we develop a fast running model for the resistance on a ground vehicle overriding both small vegetation like grass and larger vegetation like bamboo and trees. We implement of our override model into a 3D simulation environment, the MSU Autonomous Vehicle Simulator (MAVS), and demonstrate how this model can be incorporated into real-time simulation of autonomous ground vehicles (AGV) operating in off-road terrain. Finally, we show how this model can be used to simulate autonomous navigation through a variety of vegetation with a PID speed controller and measuring the effect of navigation through vegetation on the vehicle speed.
Goodin, Christopher, Moore, Marc N., Hudson, Christopher R., Carruth, Daniel W., Salmon, Ethan, Cole, Michael P., Jayakumar, Paramsothy, English, Brittney
Space Laser Tech Maps Mysterious Nearshore TerrainTBMG-535468/1/2025
Despite all the technological evolution in navigation, waters just off coastal shores around the globe have remained a black box. That is, until researchers from The University of Texas at Austin and Oregon State University developed a new technology that uses satellites in space to map out these tricky areas.
Unfurling Antennas Let Tiny Satellites Do Big ThingsTBMG-534207/1/2025
As NASA’s Artemis missions build out infrastructure on and around the Moon in the coming years, CubeSats and other small satellites will likely play an important role in a communications network that will enable not only conversation with mission control but also navigation, direct scientific observations, and more, all enabled by an internet-like “LunaNet.” These little satellites are cheap to launch and can form constellations for relaying signals reliably. But their small size makes it hard for them to carry antennas large enough to communicate across vast distances.
Assessment of Cooperative Navigation Algorithms for CAVs in Threat Scenarios12-09-01-00076/14/2025
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid
Khan, Rahan Rasheed, Hanif, Athar, Ahmed, Qadeer
Optimal Geometrical Planning of Autonomous Vehicle Trajectory2025-01-50376/10/2025
Trajectory planning is a major challenge in robotics and autonomous vehicles, ensuring both efficient and safe navigation. The primary objective of this work is to generate an optimal trajectory connecting a starting point to a destination while meeting specific requirements, such as minimizing travel distance and adhering to the vehicle’s kinematic and dynamic constraints. The developed algorithms for trajectory design, defined as a sequence of arcs and straight segments, offer a significant advantage due to their low computational complexity, making them well-suited for real-time applications in autonomous navigation. The proposed trajectory model serves as a benchmark for comparing actual vehicle paths in trajectory control studies. Simulation results demonstrate the robustness of the proposed method across various scenarios.
Soundouss, Halima, Msaaf, Mohammed, Belmajdoub, Fouad
Aerospace & Defense Technology: June 202525AERP066/5/2025
IQSat: Aitech’s AI-Enabled Picosatellite for Earth Observation and Imaging Applications Pushing the Boundaries of Rapid Production of Spacecraft: Flight Results from NearSpace Launch’s TROOP and ThinSat Platforms Transforming Satellite Antenna Technology for the Next Era of Aerospace and Defense Legacy in Action: The Enduring Role of MIL-STD-1553B Are Defense Satellites Ready for Space-Based Laser Communications? Microcomb Chips Could Improve Accuracy of GPS Systems Thermoelectric Generators Could Convert Waste Heat to Clean Energy in Jet Engines and Spacecraft Improving Electric Propulsion Thrusters for Future Spacecraft Missions A Satellite to Detect Short Gamma-Ray Burst Emissions In-Orbit Testing of Autonomous ‘Swarm’ Satellites NanoAvionics Satellite Suffers Space Debris Impact, Selfie Image Shows Damage
In-Orbit Testing of Autonomous ‘Swarm’ Satellites25AERP06_106/1/2025
With 2D cameras and space robotics algorithms, astronautics engineers at Stanford have created a navigation system able to manage multiple satellites using visual data only. They recently tested it in space for the first time. Stanford University, Stanford, CA Someday, instead of large, expensive individual space satellites, teams of smaller satellites - known by scientists as a “swarm” - will work in collaboration, enabling greater accuracy, agility, and autonomy. Among the scientists working to make these teams a reality are researchers at Stanford University's Space Rendezvous Lab, who recently completed the first-ever in-orbit test of a prototype system able to navigate a swarm of satellites using only visual information shared through a wireless network. “It's a milestone paper and the culmination of 11 years of effort by my lab, which was founded with this goal of surpassing the current state of the art and practice in distributed autonomy in space,” said Simone D'Amico
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