Browse Topic: Autonomous vehicles
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.
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce costs. Simultaneously, ride comfort standards have risen due to the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically optimized for the challenges of the automotive industry. Employing this framework facilitates the derivation of highly detailed, objective descriptions of subjective ride comfort evaluations, which promotes the achievement of advanced ride comfort maturity for new vehicles in early development phases and supports the overall enhancement of ride comfort. The exemplary application of the framework to a transient, one-dimensional ride comfort phenomenon demonstrates its capability to derive robust objective models from subjective evaluations conducted with professional test drivers in a dynamic driving simulator environment.
For sustainability reasons, the automotive market is requesting 100% monomaterial noise treatments, particularly for the end-of-life recycling without any part separation operation. But also, OEMs require super light, highly performance insulating noise treatments for electric vehicles in order to extend vehicle autonomy. PP melt-blown fiber felts present good mono-material characteristics with very good absorption, but generally not so good insulation properties behind an airtight barrier due to lack of stiffness. Moreover, these PP melt-blown fiber felts are relatively expensive and not thermoformable, thus forcing them to be used as 2D die-cut parts behind existing hard or soft trims classically. The shown optimization approach proposes to return to 100% thermoformable recycled and recyclable PET formulations blending unusual coarse mechanical specific fibers, in order to optimize the viscothermal exchanges, while maintaining good mechanical properties, with microfibers for best dissipation properties bonded by bi-component fibers. The insulation properties obtained as poroelastic spring behind a barrier allow a weight reduction of -50% compared to cotton felt while being 1 dB better for the Insertion Loss values (2 dB compared to a flexible foam) and perform as well as best PP melt-blown fiber felts while being more competitive as well as thermoformable. It is possible to adjust the sound insulation properties, sound absorption and hardness (static compressibility) using optimal PET fibers formulations but also thanks to felt verticalization processes. These optimization levers will be illustrated in this paper.
In the two months since Microvision bought Luminar and acquired key tech and talent, the sensor company has been busy. In that time, they've merged key lidar units from each company and created a perception software stack to run it in a convincing demo of its ADAS and autonomous capabilities. The company is also pushing innovative lidar tech into the defense drone and antidrone markets, already working with a German defense supplier that works with NATO member countries.
Researchers from CompPair and the European Space Agency have developed a new composite material for spacecraft with an embedded healing agent. European Space Agency, Paris, France Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation. Project Cassandra - an abbreviation for Composite Autonomous Sensing and Repair - includes sensors and a heating element within a composite carbon-fiber material, allowing spacecraft to autonomously repair initial stages of damage.
The uncrewed aerial vehicle (UAV) market is advancing at extraordinary speed, reshaping both commercial and defense aviation. From small tactical systems operating at the edge of the battlefield to high-altitude uncrewed platforms conducting strategic surveillance, UAVs are now critical assets across a wide range of mission environments. Their capabilities continue to expand - carrying more sensors, flying longer missions, and navigating more contested environments. Yet this rapid innovation brings with it growing engineering pressure. UAVs are expected to be lighter, more autonomous, more modular, and more adaptable, all while maintaining near-flawless reliability. This is where heritage becomes decisive. In an era that rewards speed, heritage provides the hard-earned engineering wisdom that ensures systems do not just fly but perform predictably, repeatedly, and safely under real-world conditions.
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.
This paper presents enhancements to the supervisory controller developed for the National Research Council Canada's Bell 412 autonomous helicopter. Building on a Discrete Event System Specification (DEVS)-based framework, the updated Supervisor introduces two new operational modes-Knobs Mode and Sticks Mode-and a structured approach for managing transitions between them and the existing modes. Drawing inspiration from NASA's Flight Guidance System philosophy, the proposed design emphasizes consistency, scalability, and flexibility in handling multiple autonomy modes. Implementation results demonstrate the effectiveness of the updated architecture in supporting future expansion of autonomous mission operations in complex and dynamic environments.
Developing a comprehensive autonomy solution for the Army's current and future aircraft fleet requires a robust computational and perception capability for decision-making across the entire flight envelope without a pilot. This also requires a flight control system and infrastructure capable of executing autonomous decisions in complex mission environments. Ongoing development of automation and autonomy, utilizing a wide range of perception sensors, has been conducted on platforms such as Sikorsky's S-70 and the Army's UH-60Mx aircraft. This work builds upon previous efforts and leverages ongoing collaborations with industry, the Department of War (DoW), and the Defense Advanced Research Projects Agency (DARPA) to advance autonomous capabilities for both optionally piloted and uncrewed aircraft.
Cargo-focused autonomous Vertical Takeoff and Landing (VTOL) operations are advancing toward commercialization significantly faster than passenger missions due to a confluence of regulatory pragmatism, technical readiness, and market economics. This paper examines the commercial potential of integrating Artificial Intelligence (AI) and Beyond Visual Line of Sight (BVLOS) control into an Uncrewed VTOL Air Cargo (AI-UVAC) vehicle for dual use military and commercial logistics applications. The Piasecki KARGO II was designed specifically for these missions and is used as the basis for evaluating this capability. This AI-UVAC concept has useful commercial application in the "mid-weight Less than Truck Load (LTL)" freight market for middle-mile delivery of time-sensitive cargo in infrastructure-constrained markets. To validate the advantages of commercial freight orchestration, a multiphase pilot program is conducted to measure the effectiveness of the LogistiWerx Generative AI-Powered Freight Logistics Orchestration platform as integrated in the KARGO II uncrewed BVLOS VTOL developed by Piasecki Aircraft Corporation (PiAC).
The design, testing, and analysis of a Guided Autorotative Delivery System (GADS) for suppression of incipient wildfires is described. The GADS consists of an unpowered 1 m diameter rotor, a control unit, and a payload of 2.2 kg of fire suppressant powder. On release from a fixed-wing UAV, the rotor passively deploys and enters autorotation, decelerating the payload and allowing precise delivery of the suppressant using cyclic pitch control. A numerical model of the system was developed to calculate the trajectory of the GADS during rotor deployment and descent, in the presence of ambient wind and cyclic pitch inputs. A reduced-scale model of the rotor was tested in a wind tunnel, and an uncontrolled full-scale, 1.5 kg prototype of the GADS was fabricated and tested by dropping from a hovering quadcopter as well as a fixed-wing UAV. The full-scale drop experiments validated the deployment and autorotation stability of the system, and demonstrated that the GADS maintains descent velocities suitable for incipient fire suppression (≈ 5 m/s). Numerical predictions indicate that the GADS descent trajectory can be controlled with cyclic pitch in an ambient crosswind of at least 5 m/s (10 kts). Measurements captured during the drop tests using onboard instrumentation show good qualitative agreement with numerical predictions. Future work will include drop tests with remotely controlled cyclic pitch, followed by fully autonomous controlled descent. The study establishes design guidelines for guided autorotative systems and illustrates their potential for scalable UAV-based wildfire suppression or emergency response.
This paper presents the results of procedurally generating urban environments, characterising them and simulating a UAS flying missions within it. It shows that varied and practically inifinite new scenarios can be generated for testing UAS. It further shows how these methods can be intergrated into a wider testing framework for the robust testing of UAS.
This paper presents a spatio-temporal graph neural network (STGNN) centric approach to enable heterogeneous agents to collaborate and cooperate for different types of missions. The STGNN-centric approach and corresponding autonomy are encapsulated in the Advanced Graph-enabled Network Technology for Collaborative Autonomous Agents (AGENTCA) technology. Various decentralized and distributed control architectures are reported in the literature, but in some instances these approaches do not leverage the inherent graph network which can increase scalability to larger teams and algorithmic efficiency. Specifically, in this paper advances in artificial intelligence are leveraged to parameterize and encode optimal, or nearly optimal, swarm control techniques. For this work, the team focused on developing a diffusion-based STGNN swarm controller using imitation learning. An expert, centralized swarm control law was used to guide the STGNN during the learning process. The STGNN controller enables the swarm to follow a leader while avoiding static and dynamic obstacles and maintaining a desired separation distance from neighbors and obstacles. The approach is demonstrated in simulation with hundreds of agents and in flight tests with up to thirteen test vehicles.
This paper develops and tests a feature-based autonomous landing system for vertical lift aircraft on stochastically moving ship decks, under degraded visual conditions. The system is tested with a custom-built quadrotor on a six-degree-of-freedom 1.5-ton Stewart platform reproducing stochastic motions up to Sea State typical of DDG-51-class ships. Experiments began with nominal conditions, followed by a stepwise degradation of deck features through occlusion, low illumination, water distortion, and glare. The vision algorithm tracked the platform and achieved landing across all scenarios, with tracking errors of up to 14% of the vehicle footprint, and up to 2.2° of pitch and roll. Overall, it demonstrated the ability to land in a GPS/Lidar-denied, difficult environment with on-board vision alone, achieving deterministic, repeatable results.
This paper presents the results of a flight test effort examining fully autonomous shipboard operations for small unmanned aerial vehicles (UAVs). Experiments were conducted at the Maneuvering and Seakeeping Basin (MASK) located at the Naval Surface Warfare Center, Carderock Division using custom-built quadrotor UAVs landing on an unmanned surface vessel (USV). These tests build upon previous ship landing algorithm testing in order to expand the envelope of operations and be more representative of a real-world mission. Several new flight modes were implemented, including takeoff and pattern flying, and a finite state machine was developed to allow smooth and autonomous transition between the different flight modes. The results from testing show smoothly executed missions both in still water and in the presence of waves. However, it was found that the initial conditions for the command filters in the position controller needed to be carefully selected. Without the correct initial conditions, discontinuities in the commands were seen when switching between modes that used the command filters and modes that bypassed them. The results of this work will help bridge the gap between ship landing-specific research and real-world applications encompassing multiple flight modes.
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the accuracy and reliability of atmospheric visibility estimation, which is crucial for improving safety in applications such as aviation, maritime navigation, and autonomous vehicles. By addressing challenges such as data variability, environmental factors, and the inherent complexity of atmospheric conditions, MAVEN contributes to more reliable and robust visibility estimation systems, thereby enhancing safety and operational efficiency in critical environments.
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