Browse Topic: Transportation Systems
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
To address the limitations of the traditional A* algorithm in lane-level navigation, we propose an autonomous vehicle path planning algorithm based on high-precision maps and an improved A* algorithm to ensure effective application in complex traffic environments. We construct a hierarchical high-precision map based on the Lanelet2 framework to achieve structured modeling of complex road environments. To address the adaptability issues of the A* algorithm in lane-level navigation, we propose optimization schemes, including heuristic function improvements, path segment division, and target point validity verification, to ensure that vehicles can autonomously change lanes on multi-lane roads. By combining dynamic programming (DP) and quadratic programming (QP), we ensure the safety and smoothness of the path. Simulation results demonstrate that the optimized algorithm enables smooth stopping and starting at traffic lights in structured road environments and autonomous lane changes on multi-lane roads. Compared to using DP alone, QP provides smoother and safer driving paths and exhibits superior obstacle avoidance performance in speed planning. This method effectively ensures the rationality of path planning in complex road environments while strictly adhering to traffic rules, thereby enhancing the safety and reliability of path planning.
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest regression model showed that time characteristics accounted for 89.2% of the variance of accident frequency interpretation, significantly exceeding the contribution of geographical factors (10.2%) and location attributes (0.6%). After hyperparameter optimization, the accuracy of XGBoost classifier in predicting serious accidents is 75.97%, and the AUC value is 0.8412, which has strong robustness, and provides reliable support for dynamic risk assessment of traffic management system.
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For analysing flow and acoustic induced structural vibration, a fully run time coupled framework combining a hybrid CFD-CAA approach with a modal response simulation was validated and presented at the ISVNH 2022 (SAE Technical Paper 2022-01-0938). In this paper i We apply this CFD–CAA–modal coupling method to a series-representative bonnet geometry and demonstrate its capability to capture flow and aeroacoustically driven vibration with two-way coupling. ii We analyse the modal properties of the bonnet and show that confined air volumes beneath the bonnet can introduce significant fluid loading effects, which are already embedded in experimentally validated FE modal models and must therefore be treated carefully in two-way coupled simulations. iii We validate the fully coupled aeroelastic simulation against wind-tunnel measurements with undisturbed inflow, show close agreement with the measured vibration response and analyse that the dominant excitation is in this case from below the bonnet due to acoustic pressure fluctuations.
There's a well-known video from San Francisco in 1906 that comes up repeatedly in mobility discussions here in the 21st Century. If you haven't seen A Trip Down Market Street, it depicts the absolute bonkers variety of transportation methods used on Market Street back then: cable cars, horsecars, streetcars, pedestrians, automobiles and more. Past is prologue in a world that is adding scooters, delivery robots and other last-minute delivery vehicles to our streets. At the 2026 New York International Auto Show in April, Honda displayed its latest option in the form of the Fastport eQuad Prototype. The eQuad was originally unveiled at Eurobike 2025 and technically comes from Fastport, a micromobility venture from the Honda New Business Innovation Lab that was established to work on projects with global logistics companies. Jamie Davies, chief of operations for Fastport, called the group a kind of startup within Honda. “Three years ago,” Davies told SAE Media in New York, “a small group of Honda associates [came] together and [said], Okay, how can we create a new value for the company, a new business vertical? And so we've run the project in an agile way, working with customers all along the way to understand what their needs are, what the requirements are, and to bring to market something that fits.”
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is designed. Deformation analysis under the representative working conditions are evaluated through finite element software. The test results show that the maximum deformation of this docking structure under typical load is significantly lower than the docking tolerance and positioning repeatability requirements. The structural stiffness and stability meet the design indicators. The above work indicates that the proposed autonomous navigation strategy and the docking structure for the intelligent chassis can effectively support the modular operation of “air trunk & ground terminal” mode, providing a scientific basis for the functional integration and system reliability research of split-type flying vehicles.
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential speed. The overall design satisfies the requirements of safety and strength through mechanical verification and stress analysis. The proposed system can help the elderly people to recover from falls better and enhance their independence and safety in their daily walks.
Advanced air mobility (AAM) seeks to develop a large-scale transportation system to revolutionize how people live and work, with electric vertical take-off and landing (eVTOL) aircraft serving a central role due to reduced emissions and noise impact. An important aspect for eVTOL aircraft certification is safe urban operations, which require understanding of the response due to aerodynamic disturbances. Experimental data are required to support eVTOL aircraft development with respect to flight dynamics and controllability, as well as design specification development. While flight testing of the full-sized air vehicle will be necessary as part of the certification process, subscale testing offers many advantages with respect to cost and flexibility, in addition to examining operational conditions that one would be reluctant to test in flight at full scale such as emergency conditions. These advantages only may be seen if the underlying scaling principles of flight dynamics / control, aerodynamic interactions, and propulsion-airframe integration are understood. This paper describes initial work towards development of a general subscale testing methodology for eVTOL aircraft flight dynamics and disturbance response characteristics including limited degree of freedom (DOF) and free flight testing. An overview of the initial development work is provided, including discussion of scaling relationships, subscale air vehicle model development, and testing activities focusing on flying qualities and stability / control characteristics.
This paper introduces a novel concept for an AI-powered system designed to manage vertiport takeoffs and landings by proactively addressing the safety-critical issues of downwash and outwash. The proposed system utilizes a stream of live feedback from on-site sensors, combined with a robust predictive modeling engine, to generate optimal, aircraft-specific approach and landing trajectories in real-time. By leveraging a comprehensive database of pre-computed downwash/outwash scenarios for a multitude of UAM aircraft configurations, the AI can accurately predict the unique outwash operational footprint for each individual landing operation, based on the approaching aircraft and under the prevailing conditions. This powerful predictive capability allows the system to calculate and assign an optimal approach vector that actively minimizes risk by directing hazardous airflows away from personnel, active walkways, and other sensitive ground assets. This represents a paradigm shift from static, reactive safety measures to a proactive, intelligent, and performance-based operational model; thereby paving the way for the safe, efficient, and large-scale implementation of UAM aircraft and air taxi operations.
Electric vertical takeoff and landing (eVTOL) aircraft have rapidly emerged as a potential promising solution for sustainable and scalable urban air mobility. These vehicles are at a crucial stage of development, with sub-scale and full-scale flight test campaigns already underway. In forward flight, critical structural components of these variable RPM rotary wing aircraft can experience strong time varying aerodynamic loads. These components are lightweight, stiff, and often have very little intrinsic damping. Thus, from a life-cycle perspective, vibratory strains are of significant concern. This work investigates the use of Impact Dampers (IDs) in attenuating the bending and torsional response of a characteristic eVTOL boom. A computational reduced order model of an ID is developed using the Hunt-Crossley nonlinear contact model and linear beam finite elements. Predicted damper performance is compared with prior experimental measurements. Parametric studies are performed by varying key damper design variables, and predicted trends are shown to be consistent with experimentally observed behavior. The IDs attenuated the system structural response at resonance by 60.7% in bending and 52.1% in torsion, for a 5% mass penalty. Building on works presented at the 80th and 81st Vertical Flight Society Annual Forums, this publication is a next step towards establishing IDs as a viable multi-modal passive vibration control device in variable RPM multirotor vehicles.
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