Collision Risk Research Articles

An improved uncooperative space target de-tumbling method using electromagnetic de-tumbling devices with AC excitation

A large amount of space debris with rotational motion occupies limited orbital resources, necessitating a de-tumbling phase before the capturing phase to mitigate collision risks. To enhance the safety and performance of de-tumbling uncooperative space targets, this paper proposes and investigates a contactless method using electromagnetic de-tumbling devices with alternating current (AC) excitation, which can generate larger de-tumbling torque compared to conventional direct current (DC) excitation. First, the optimal configuration of the devices for de-tumbling the target is studied under the constraint of constant power consumption. Then, the mechanism of de-tumbling torque generated by AC excitation is analyzed, leading to the development of the optimal AC excitation de-tumbling method. Subsequently, the optimal configuration and AC excitation de-tumbling method are validated through finite element method simulations, confirming the advantages of the proposed method. Finally, ground experiments demonstrate the superiority of AC excitation over DC excitation.

Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach

A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.

Navigators’ Perspective on Information Requirements for Supervisory Control of Autonomous Ships

This study explores the application of agent transparency in the context of autonomous ships. Four levels of transparency were developed depicting decisions, planned actions, reasoning, and input parameters of a collision and grounding avoidance system in a realistic navigational context. Thirty-four licensed navigators were provided with Human Machine Interface concepts depicting four levels of transparency. Qualitative feedback was obtained through semi-structured interviews about which information they felt is needed to supervise autonomous ships safely and effectively. In addition, the participants’ ranked the HMIs according to their preferences. The results indicate the need for depicting the outcomes of the system’s collision risk analysis for supervisory control. Furthermore, the results illustrate the variations in supervisory strategies and the resulting dilemma for the amount and type of information required to support supervisors. Finally, this study highlights the importance of expert knowledge in the design of safety critical systems.

Efficiency of Group Movement within Human Risk Perception

A braking index that incorporates the risk perception of human drivers has been developed to estimate how many drivers would brake in a given situation. We used agent simulations to verify whether incorporating the braking index can improve the efficiency of the agents’ movement in the whole space. The completion time and the number of collisions were measured to compare the simple avoidance algorithm without the braking index and the proposed algorithm with it. While a simple avoidance algorithm showed a strong trade-off between the completion time and the number of collisions, the proposed algorithm resulted in an efficient movement with a shorter time and fewer collisions. More efficient movement could be achieved by incorporating human risk perception because the risk of collision with others was individually evaluated according to others’ position and direction, and only as much avoidance as necessary was performed.

Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm

With the rapid development of the economy and the continuous improvement of people’s living standards, the printing and packaging industry plays an increasingly important role in people’s lives. The traditional printing industry is a discrete manufacturing industry, relying on a large amount of manpower and manual operation, low production efficiency, higher labor costs, wasting of resources, and other issues, so the realization of printing factory intelligence to improve the competitiveness of the industry is an important initiative. Automatic guided vehicles (AGVs) are an important part of an intelligent factory, serving the function of automatic transportation of materials and products. To optimize the movement paths of AGVs, enhance safety, and improve transportation efficiency and productivity, this paper proposes an alternative implementation of the A* algorithm. The proposed algorithm improves search efficiency and path smoothness by incorporating the grid obstacle rate and enhancing the heuristic function within the A* algorithm’s evaluation function. This introduces the evaluation subfunction of the nearest distance between the AGV, the known obstacle, and the unknown obstacle in the global path in the dynamic window approach (DWA algorithm), and reduces the interference of obstacles with the AGV in global path planning. Finally, the two improved algorithms are combined into a new fusion algorithm. The experimental results show that the search efficiency of the fusion algorithm significantly improved and the transportation time shortened. The path smoothness significantly improved, and the closest distance to obstacles increased, reducing the risk of collision. It can thus effectively improve the productivity of an intelligent printing factory and enhance its flexibility.

Risk Assessment of Bird Collisions with a Wind Turbine Based on Flight Parameters

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Drivers’ situational awareness of surrounding vehicles during takeovers: Evidence from a driving simulator study

This study aimed to understand the influence of surrounding vehicle configuration, driving lane, and traffic density on drivers’ situational awareness (SA), takeover performance, and eye-tracking behaviors in conditionally automated driving. An experiment was conducted with the participation of 40 university students using a fixed-base driving simulator configured to simulate SAE Level 3 automation. During the experiment, participants were engaged in playing Tetris on a tablet as a non-driving related task in automated driving mode. Upon hearing an auditory takeover request, participants were instructed to take control of the vehicle, and then complete a scene reconstruction task to report their SA after transferring control back to the automated driving system. Our findings showed that drivers often neglected vehicles at their sides and rear during the takeover, which was associated with higher collision risks. Higher oncoming traffic density led to drivers’ worse SA of surrounding vehicles but more cautious driving behavior. Driving in the right lane generally resulted in smoother takeovers with lower collision risks. Interestingly, while SA did moderate the impacts of driving conditions on safety margins, a higher level of SA did not consistently relate to improved performance, especially in complex scenarios. This suggests the need for support systems that guide drivers to focus on safety–critical objects rather than simply amplifying SA in general. These insights have significant implications for the design of driver monitoring and support systems in automated vehicles.

Two-step dynamic obstacle avoidance

Dynamic obstacle avoidance (DOA) is a fundamental challenge for any autonomous vehicle, independent of whether it operates in sea, air, or land. This paper proposes a two-step architecture for handling DOA tasks by combining supervised and reinforcement learning (RL). In the first step, we introduce a data-driven approach to estimate the collision risk (CR) of an obstacle using a recurrent neural network, which is trained in a supervised fashion and offers robustness to non-linear obstacle movements. In the second step, we include these CR estimates into the observation space of an RL agent to increase its situational awareness. We illustrate the power of our two-step approach by training different RL agents in a challenging environment that requires to navigate amid multiple obstacles. The non-linear movements of obstacles are exemplarily modeled based on stochastic processes and periodic patterns, although our architecture is suitable for any obstacle dynamics. The experiments reveal that integrating our CR metrics into the observation space doubles the performance in terms of reward, which is equivalent to halving the number of collisions in the considered environment. We also perform a generalization experiment to validate the proposal in an RL environment based on maritime traffic and real-world vessel trajectory data. Furthermore, we show that the architecture’s performance improvement is independent of the applied RL algorithm.

Automatic Excavation System with Multiple Excavators in the Pneumatic Caisson Method

In this study, we propose an automated excavation system for pneumatic caisson construction using either a single excavator or multiple excavators. The system divides the work area among the excavators and manages the transition of occupied states in shared areas to reduce the risk of collisions. Additionally, the Laplace potential method is employed for path planning to avoid collisions with equipment inside the caisson. The system also includes a mechanism for disposing of soil outside the caisson by dumping it into an earth bucket. To confirm the effectiveness of the proposed method, verification tests were conducted: one using a single excavator in a narrower-than-usual caisson, and another using two excavators in a test field. These tests demonstrated the method’s effectiveness.

Machine-learning-based ensemble regression for vehicle-to-vehicle distance estimation using a toe-in style stereo camera

Adjusting the following distance from the front vehicle in highway traffic is important to reduce the risk of collision. Distance estimation is an important research area for advanced driver assistance systems. Therefore, this paper presents a methodology that combines the strengths of several machine learning algorithms using joint decision mechanisms and searches for optimal results for vehicle-to-vehicle distance estimation. The hyperparameter optimization of machine learning models is performed by an iterative algorithm that compares combinations of hyperparameter values. In addition, machine learning algorithms are combined and tested with ensemble learning methods to improve the results obtained. According to the experiments, the ensemble voting regression created by combining extreme gradient boosting, categorical boosting and two multi-layer perceptron models achieves the best result with a mean absolute percentage error value of 1.5444. Considering the comparisons made with other methods, the accuracy of the results obtained with the proposed method is quite high.Code is at: https://github.com/ozgurduran/V2V-VR.

Urbanisation has impacted the population genetic structure of the Eurasian red squirrel in Japan within a short period of 30 years

Human activities often induce maladaptive effects in urban wildlife, such as increased inbreeding and decreased gene flow, consequently leading to a loss of biodiversity. Although maladaptive effects are commonly reported in urban wildlife, some wildlife species are not affected. Understanding the conditions and factors that affect the genetic structure of wildlife is crucial for its conservation in urban environments. Eurasian red squirrels Sciurus vulgaris in Obihiro City, Hokkaido, Japan, have settled in large residential lots with a high risk of vehicle collisions for approximately 30 years, raising the possibility that the urban population would be genetically isolated from nearby rural populations and may experience a rapid decrease in genetic diversity. In this study, we analysed the population structure, genetic distance, and nucleotide diversity of squirrels in the Obihiro area using genome-wide single nucleotide polymorphisms (SNPs). Principal component analysis and ADMIXTURE showed that urban and rural squirrel populations formed distinct clusters with intermediate urban and rural boundary populations. Genetic distances between the urban populations were greater than that of other pairs. Nucleotide diversities were lower in urban populations than in other populations, suggesting limited gene flow between urban and other populations, possibly due to urbanisation. The change in the genetic characteristics of urban squirrel populations during this short period demonstrates the strong impact of urbanisation. Our findings suggest that a genome-wide approach to the genetic structure of wildlife contributes to its conservation in urban environments.

Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios.

A new wake superposition model and its application in collision prevention control of tri-riser array system

In the field of offshore oil exploitation, the compact multi-riser arrangement increases the risk of collision and has an adverse impact on production and the marine environment. The solution to this problem involves accurate motion prediction and collision prevention control under the effect of flow field superposition between risers, but there is little research on this related theory. This paper improves the traditional wake shielding effect model and further proposes a wake shielding superposition effect model for the mutual influence between risers, in order to establish a comprehensive accurate motion control model for tri-riser array system. A solution method is introduced for this model, both vibration and collision analysis are conducted for the system. The study revealed that solely augmenting the spacing inadequately mitigates system collisions, and active control is an inevitable choice. Based on this, this paper further designs an active boundary controller that is easy to implement in engineering, to achieve collaborative collision prevention control for three risers, and verifies its stability. Compared with traditional PD control, the proposed control method exhibits significant superiority, which can effectively reduce vibration amplitude, significantly increase dynamic spacing, and thus effectively prevent collisions between risers.

Monitoring model for enhancing adaptability in human–robot collaborative mold assembly

ABSTRACT Molds are assembled manually due to their inherent characteristics of low-volume and high-variety production. Given the ergonomic risks caused by heavy-handling and repetitive tasks and diverse requirements in mold assembly, collaborative robots offer adaptability and ease of reconfiguration, making them potential solutions to these challenges. This study introduces a monitoring model for human–robot collaborative mold assembly using two cobots. This model encompasses manual progress monitoring for cobot execution and position-sharing modules. Manual task actions are detected using the You-Only-Look-Once-v8 Nano model. Detected actions are subsequently classified into different states. These identified states are relayed to the cobots, enabling early execution and controlling the cobots’ entry into the assembly area. This study proposes a position-sharing approach to prevent collisions by receiving coordinates via Modbus between the two cobots. Most existing research has developed separate models for action and part recognition, excluding the utilization of recognition results to enable early execution. This study contributes to a novel model for monitoring manual progress to enable early execution of subsequent tasks and facilitate communication through position sharing during human–robot collaborative mold assembly tasks. The results show that assembly time and the risk of collisions between cobots can be reduced in human–robot collaborative mold assembly tasks.

Alternative Emergency Vehicle Lighting Affects Traffic Behaviors

Emergency vehicle lightings (EVL) mitigate the risk of collision and make drivers move over. The present paper explored how new alternative EVL designs can improve driver behavior in different emergency vehicle interaction scenarios. From workshops with 14 emergency responders, three scenarios (Emergency driving, Police pull-over, Emergency vehicle alongside the road) and 19 EVL blink patterns were chosen. These alternative EVLs were presented in an online survey with 2627 Swedish respondents. Alternative EVL affected reported driving behavior. Drivers reported being most certain of what is expected from them in the emergency-driving scenario. It is important to ensure that the EVL is easy to interpret in more unfamiliar emergency-vehicle interactions such as police pull-over. Only a third of drivers had seen the pull-over EVL currently used in Sweden. The current pull-over EVL in combination with a stop sign increased the chance of drivers reporting that they would pull over.

Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles

Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method’s suitability for assessing AV performance in dynamic traffic environments.

Research on ship navigation strategy in dynamic sea ice environments based on flexibility velocity obstacles algorithm

Sea ice constitutes one of the primary risks in Arctic navigation. Currently, how to maneuver a ship within a sea ice environment relies predominantly on the experience of the pilot. In this paper, based on satellite image data and an improved sea ice target matching approach, a dynamic ship navigation environment within the sea ice zone is established. Subsequently, we developed a sea ice collision risk index (ICRI), which takes into account the relative motion relationship between the sea ice and the ship as well as the sea ice area parameter. Finally, a flexible velocity obstacle (FVO) algorithm is proposed by the Arctic ice navigation proposal, enabling the optimization of ship routes and preventing ships from passing through the central region of large floating ice. The results show that the method proposed in this paper successfully constructs the navigation environment model in the drifting ice region, and can provide real-time maneuvering guidance for ships, effectively reduce the risk of sea ice collision, and optimize the navigation path planning in the ice region. It can improve the safety and pre-planning efficiency of ship navigation in the ice region.

Risk of pedestrian collision for persons with peripheral field loss: A computational analysis.

People with peripheral field loss report colliding with other pedestrians on their blind side(s). We show that, in dyadic collision scenarios between persons, one with field loss, such as homonymous hemianopia, and the other normally sighted pedestrian, collisions occur only if the persons with homonymous hemianopia are overtaking the pedestrians, and the collision risk is concentrated at farther bearing angles than previously suggested. Prior work computed the risk of collision while simulating both pedestrians as points and did not consider the ability of the other pedestrian's normal vision to avoid the collision. We extended the model to better characterize the open space collision risk posed for persons with homonymous hemianopia by normally sighted pedestrians where both have volume. We computed the risk of collision with approaching pedestrians using a model that simulates approaching pedestrians as volumetric entities without vision, volumetric entities with vision, and as points for comparison with the prior work. Collision risk of approaching pedestrians is characterized for all three conditions through spatial collision risk maps and collision risk densities as a function of bearing and radial distances. The collision risk for volumetric pedestrians is slightly different from that of point pedestrians. For volumetric pedestrians simulated with normal vision, the risk of collision was reduced substantially, as the other pedestrians could detect and avoid most impending collisions. The remaining collision risk is from pedestrians approaching at higher bearing angles (>50°) and from shorter radial distances (<2 m). Thus, collisions occurred when the pedestrians started in front of the person with homonymous hemianopia that was overtaking the pedestrian. The probability of collisions between pedestrians and the person with peripheral field loss is low and occurs only when the person with peripheral field loss is walking from behind the pedestrian at faster speed, thereby overtaking them. Such collisions occur with pedestrians at higher bearing angles, which should be monitored by assistive aids to avoid collisions. The same collision risk applies not only in homonymous hemianopia but also in other peripheral field loss such as monocular vision loss or concentric field loss, as common in retinitis pigmentosa and glaucoma.

Establishing requirements for lunar and cislunar orbital debris tracking

There is a need to formally coordinate lunar and cislunar space traffic management to mitigate the risk of collisions with micrometeoroids and orbital debris in this space domain. To control this hazard, it will be critical to develop a high-fidelity orbital debris catalog. This catalog will be maintained by monitoring and propagating the trajectory of objects. The need to perform a debris avoidance maneuver for collision avoidance will depend on the fidelity of the propagated debris trajectory. A larger uncertainty (magnitude as a function of time) will require a larger maneuver and a higher likelihood of the need to perform this maneuver. This study assesses debris avoidance maneuvers and corresponding corrective actions to recover the desired mission trajectory as a way to evaluate the desired capability of tracking of objects in lunar and cislunar orbit. The baseline Earth-to-Moon trajectory was that of the Artemis I mission. Typical conjunction assessments were postulated at 100,000 km from Earth's center (approximately a quarter of the way to the moon) and at lunar orbit insertion (LOI). For these two cases, the required debris avoidance maneuver (and hence impact to the mission) is tied to the uncertainty in orbital debris tracking. The study provides a methodology and baseline inputs to establish future requirements of debris tracking in lunar and cislunar orbits. It also reinforces the importance of long-term sustainability for lunar missions, specifically preventing the generation of orbital debris.

Leveraging Copernicus for Ice Block Detection: A Step Towards Safer Arctic Navigation

Background The Arctic is becoming more accessible due to ice melting, posing challenges for navigation due to floating ice blocks. Detecting these ice blocks is crucial for ensuring the safety of vessels navigating these waters. Methods This study introduces a novel method for detecting ice blocks in the Arctic using Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) satellite images from the Sentinel-1 mission of the Copernicus program. Our approach combines state-of-the-art image segmentation and deep learning techniques. We utilize a Convolutional Neural Network (CNN) classifier to accurately locate ice blocks within the images and retrieve their geographic coordinates. The method's performance is validated using precision, recall, and F1-score metrics. Results Our CNN classifier demonstrates robust performance in detecting ice blocks, validated through precision, recall, and F1-score metrics. The practical application of our technology was showcased in the AI-ARC project’s Arctic demo, receiving positive feedback from coast guards across various European countries. The system provides near-real-time alerts about detected ice blocks, allowing for timely route adjustments and reducing collision risks. Conclusions The developed system significantly contributes to Arctic navigation safety by providing accurate and timely detection of ice blocks. This work underscores the transformative potential of AI in environmental monitoring and maritime safety. Future refinements will be based on user feedback and advancements in AI technology, enhancing the system's effectiveness and reliability.