Collision Risk Research Articles

Research on Safety Control Technology for Dual Robots Based on Kinematic Models

The industrial robots play an important role in modern industrial manufacturing processes, and their applications are becoming increasingly widespread, greatly improving manufacturing efficiency and significantly enhancing the digitalization level of the manufacturing industry. Therefore, achieving safe control of multi robot collaborative work is of great significance for the stable and efficient operation of robot systems. This article proposes a dual robot safety control technology based on the kinematic model of industrial robots. Firstly, a mathematical model of the kinematic space of the dual robots is constructed using D-H parameters; Then, use recursive traversal algorithm to calculate the minimum distance between each joint arm of the two robots in real time; Finally, the minimum distance between each joint arm will be calculated and compared with the set safety threshold to achieve real-time assessment and control of collision risk during the movement of the dual robots. A dual robot safety control testing platform was built using two KUKA industrial robots, integrated control software was developed, and relevant algorithms were validated. Through experiments, it was verified that this method can evaluate the collision risk of dual robots in real time during motion and effectively control the collision risk during the motion of dual robots.

Research on the Collision Risk of Fusion Operation of Manned Aircraft and Unmanned Aircraft at Zigong Airport.

Low-altitude airspace is developing rapidly, but the utilization rate of airspace resources is low. Therefore, in order to solve the problem of the safe operation of the fusion of large UAVs and manned aircraft in the same airspace, this paper analyzes the theoretical calculation of the collision risk of the fusion operation of manned aircraft and UAVs at Feng Ming Airport in Zigong, verifying that while assessing the safety spacing of 10 km in the lateral direction, it further simulates the possibility of calculating the theoretical smaller safety spacing. The study will propose a new theory of error spacing safety margin and improve it according to the traditional Event collision risk model, combining the error spacing safety margin to establish an improved collision model more suitable for the fusion operation of manned and unmanned aircraft and reduce the redundancy of calculation. The error factors affecting manned and unmanned aircraft at Zigong Airport are analyzed, and theoretical calculations are analyzed by combining the actual data of Zigong Airport. Finally, the Monte Carlo simulation method is used to solve the error, substitute the calculation results, and simulate a section of the trajectory of the fusion operation for the reverse argument. The theoretical calculation results show that the collision risk from 10 km to 8 km satisfies the lateral target safety level (TSL) specified by ICAO under both traditional and improved models. The collision risk calculated by the improved model incorporating the error spacing safety margin is smaller, which enhances the safety of the model calculations. The results of the study can provide theoretical references for the fusion operation of manned and unmanned aircraft.

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks.

Impacts of a Toll Information Sign and Toll Lane Configuration on Queue Length and Collision Risk at a Toll Plaza with a High Percentage of Heavy Vehicles

Impacts of a Toll Information Sign and Toll Lane Configuration on Queue Length and Collision Risk at a Toll Plaza with a High Percentage of Heavy Vehicles

Crash Risk Monitoring Method for Highways in Rain Conditions

The slick road surface during rainy days necessitates an increased braking distance, posing prominent safety hazards for driving. Consequently, this paper researched on the monitoring method of collision risk posture of highway in rainy days. The impact of rain on the driving risk of vehicles is mainly the braking distance. In this paper, the ratio μ, defined as the quotient of stopping sight distance (St) and the spacing (Sr) between the front of a trailing vehicle and the rear of a leading vehicle, was adopted as the collision risk assessment metric. Employing the Expressway A-Rain segment from the Citysim dataset as the subject of investigation, traffic risk probability statistics diagrams and heatmaps had been generated. The results showed that the ratio μ>1 has accounted for nearly 10% of the roadway, the traffic risk of this roadway is high, and the risk is mainly distributed on the middle lane of the freeway and entrance ramp.

Numerical investigation of impact response of bridge pier subjected to off-center vehicle collision

The increased risk of vehicle collisions on bridge piers causes damage to bridge structures and traffic congestion. In extreme cases, severe collisions caused by trucks lead to bridge collapse and loss of lives. Many studies have been conducted to investigate the dynamic responses of bridge piers subjected to vehicle collisions. Most of them focused on head-on (center-to-center) collisions based on the assumption that they represented the worst scenarios. However, off-center collisions were most often observed in vehicle collision accidents. In this study, bridge piers with different cross-section geometries (i.e., circular, and square) subjected to off-center vehicle collisions are numerically investigated. The results show that off-center vehicle collisions result in more severe spalling damage to the circular piers than head-on collisions. However, the curved surface of the circular piers effectively avoids inducing torsional response under off-center collisions. On the other hand, off-center collisions on square piers induce torsional response, thus more significant deformation, and damage than head-on collisions. Further mechanism analyses are conducted to explain the influence of off-center collisions, focusing on truck type and rectangular cross-section. Furthermore, some meaningful suggestions are put forward to resist off-center vehicle collisions for bridge piers in design process or in service.

Hybrid trajectory planning of two permanent magnets for medical robotic applications

Independent robotic manipulation of two large permanent magnets, in the form of the dual External Permanent Magnet (dEPM) system has demonstrated the possibility for enhanced magnetic control by allowing for actuation up to eight magnetic degrees of freedom (DOFs) at clinically relevant scales. This precise off-board control has facilitated the use of magnetic agents as medical devices, including catheter-like soft continuum robots (SCRs). The use of multiple robotically actuated permanent magnets poses the risk of collision between the robotic arms, the environment, and the patient. Furthermore, unconstrained transitions between actuation inputs can lead to undesired spikes in magnetic fields potentially resulting in unsafe manipulator deformation. This paper presents a hybrid approach to trajectory planning for the dEPM platform. This is performed by splitting the planning problem in two: first finding a collision-free physical path for the two robotically actuated permanent magnets before combining this with a path in magnetic space, which permits for a smooth change in magnetic fields and gradients. This algorithm was characterized by actuating each of the eight magnetic DOFs sequentially, eliminating any potential collisions and reducing the maximum undesired actuation value by 203.7 mT for fields and by 418.7 mT/m for gradients. The effect of this planned magnetic field actuation on a SCR was then examined through two case studies. First, a tip-driven SCR was moved to set points within a confined area. Actuation using the proposed planner reduced movement outside the restricted area by an average of 41.3%. Lastly, the use of the proposed magnetic planner was shown to be essential in navigating a multi-segment magnetic SCR to the site of an aneurysm within a silicone brain phantom.

Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments.

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio-temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio-temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments.

A New Multimodal Map Building Method Using Multiple Object Tracking and Gaussian Process Regression

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels.

Situational NMAC risk assessment using conditional random field for UAV tiered aerial passage operations

Complex urban unmanned aerial vehicle (UAV) operations necessitate near mid-air collision (NMAC) risk assessment to ensure safety. In this work, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. Firstly, a concept of tiered aerial passages is designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity. For UAV tiered aerial passage operations, a situational NMAC risk assessment framework is formulated, enabling the host UAV to evaluate and respond to real-time risks, ensuring avoidance of potential collisions. Specifically, a probabilistic risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the NMAC risks in overtaking, approaching head-on, and converging UAV situational encounters are quantified considering advisory, caution, and warning risk alert levels. To resolve risks assessed, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed method is validated by testing encounter situations and comparative study. The results show that the proposed method outperforms UAV NMAC risk assessment, effectively tailoring different risk requirements for diverse UAV operations.

NFL’s dangerous strategies of marketing football to youth: shades of big tobacco

ABSTRACT Comparisons have been made between the tobacco industry’s historic tactics in defending their products with the responses of some key actors in the sports world to head injuries. Both, it is said, have deployed deceptive marketing and advertising techniques to entice youth to engage with a subjective pleasure-producing product that has undeniable short- and long-term health detriments. Unlike what is called euphemistically, ‘Big Tobacco’, however, the National Football League (NFL) has evaded legal restrictions on the promotion of an inherently dangerous product that targets youth. By contrast, in 1997, the Federal Trade Commission charged tobacco company R.J. Reynolds with violating federal law by using a cartoon character, Joe Camel, to allure children under age 18. The NFL has partnered with Nickelodeon to produce programs such as NFL Rush Zone and NFL Slimetime that target youth by using cartoon mascots, bright colors, 3D iconography, and other child-friendly symbols. In addition to fostering positive associations between tackle football and cartoons, these broadcasts have explicitly minimized the risks of tackling. For example, one NFL Playoffs on Nickelodeon announcer equated a forceful head collision with a scrape of the knee that a child might experience during school recess. The NFL’s marketing strategies deflect public attention away from the risks of repetitive head collisions, concussions, traumatic brain injury (TBI), and chronic traumatic encephalopathy (CTE), portraying football as an appealing activity. The ongoing connections between the NFL and youth-oriented television programming raise crucial questions about how risk is communicated in a sport with inherent dangers of long-term cognitive damage. As long as tackle football is portrayed alongside beloved cartoon characters such as Spongebob Squarepants, impressionable children are likely to form undilutedly positive perceptions of the NFL and ignore the substantial risks of repeated, full-body collisions when enchanted by brightly-colored slime, animated bubbles, and goofy dance moves.

Modeling Medium-Term Debris Cloud of Satellite Breakup via Probabilistic Method

Accurately modeling the evolution of debris clouds from satellite breakups is necessary for assessing the collision risk to space assets. Generally, the evolution of debris clouds can be divided into short-, medium-, and long-term phases. Compared to the short- and long-term phases, the study on the medium-term phase was insufficient. This paper proposes a probabilistic method to model the medium-term evolution of a debris cloud. First, a satellite breakup event can be characterized by a probabilistic density distribution of the initial velocity vectors of all fragments. Then, the distribution in the initial velocity can be transformed to that in three classic orbital elements, i.e., semimajor axis, eccentricity, and inclination. Given that the driving force of medium-term evolution is the J2 perturbation and that J2 has no secular contribution to these three classic orbital elements, the distribution of classic orbital elements remains stationary during the evolution. This feature can be exploited to express the probabilistic density distribution of any given target position in terms of the density distribution of the classic orbital elements by changing variables. Finally, 16 one-variable equations with analytical first derivatives were formulated to find the proper inverse mapping from the given position to the classic orbital elements, and the Jacobian matrix of the inverse mapping was derived. Medium-term debris clouds resulting from two typical breakup velocity distributions were tested using the proposed method. Results show that the proposed method can accurately simulate the evolution of the clouds’ geometrical extent and density flow.

Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer learning

Objectives Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. This study aims to develop recognition models for aggressive driving under various climate conditions, addressing the challenge of collecting sufficient data in abnormal weather. Methods Driving data was collected in a virtual environment using a driving simulator under both normal and abnormal weather conditions. A model was trained on data from normal weather (source domain) and then transferred to foggy and rainy weather conditions (target domains) for retraining and fine-tuning. The K-means algorithm clustered driving behavior instances into three styles: aggressive, normal, and cautious. These clusters were used as labels for each instance in training a CNN model. The pre-trained CNN model was then transferred and fine-tuned for abnormal weather conditions. Results The transferred models showed improved recognition performance, achieving an accuracy score of 0.81 in both foggy and rainy weather conditions. This surpassed the non-transferred models’ accuracy scores of 0.72 and 0.69, respectively. Conclusions The study demonstrates the significant application value of transfer learning in recognizing aggressive driving behaviors with limited data. It also highlights the feasibility of using this approach to address the challenges of driving behavior recognition under abnormal weather conditions.

Development of an Intelligent Road-Crossing Sensor System for Enhanced Pedestrian Safety

Pedestrian safety at road crossings in urban environments is a critical issue due to the increasing vehicular traffic and high pedestrian density. Traditional traffic control measures often fail to adequately address the dynamic interactions between vehicles and pedestrians, leading to significant risks of accidents. This study proposes the development of an intelligent road-crossing sensor system leveraging advanced technologies such as sensors, artificial intelligence (AI), and the Internet of Things (IoT). The system aims to enhance pedestrian safety by providing real-time alerts and adaptive traffic control, thereby reducing the risk of pedestrian-vehicle collisions. The methodology includes sensor selection, data collection, machine learning model development, and system implementation and testing. The potential benefits and challenges of deploying such systems are also discussed, offering insights into creating smarter and safer urban environments. Keywords: Pedestrian safety, intelligent sensor systems, road crossing, vehicle detection, traffic signals

Influence of surrounding traffic on lane change dynamics: Insights from a video-based laboratory study

The inherent complexity associated with lane-changing manoeuvres can significantly disrupt traffic flow and increase the risk of collisions. While the lane-changing vehicle influences the surrounding traffic, it is also simultaneously influenced by it. This study aims to examine how the presence and behaviour of surrounding vehicles affect the lane changer's behaviour. A study was conducted in the laboratory using video stimuli of various simulated lane-changing scenarios to achieve the research aim. The study used a two-block design. In the first block, the impact of the lag vehicle was evaluated by varying its gap and behaviour (acceleration, deceleration or maintaining speed) relative to the lane-changing vehicle. In the second block, both the lead and lag gaps were manipulated with respect to the lane-changing vehicle. Data from the participants (n=29) were collected on dependent variables, including lane change decisions (gap acceptance or rejection), perceived cooperation, and reaction time. The analysis was conducted using an Aligned Rank Transform ANOVA. The findings of this laboratory-based study suggest that the lag vehicle, specifically its behaviour, has more influence on lane change decisions than the lead vehicle in the target lane. Additionally, when a lag vehicle decelerates to create a gap in response to a lane changer's request, its actions are perceived as more cooperative, compared to when it accelerates or maintains speed. Furthermore, decisions to change lanes are made faster when the lag vehicle shows a deceleration behaviour. The results of this laboratory-based study provide valuable insights for improving current lane-changing models. We also discuss the implications of findings for improving algorithms governing autonomous vehicle interactions in mixed traffic. Finally, we discuss the benefits and limitations of laboratory-based approaches in studying causal relationships among different factors, as well as the generalizability of our findings.

Adaptive Speed Control Scheme Based on Congestion Level and Inter-Vehicle Distance

Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance (Disinter). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and Disinter. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the Disinter level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios.

The Role of Human-Robot Interaction in Enhancing Efficiency and Safety in Industrial Robotics

This study examines the role of human-robot interaction (HRI) in enhancing efficiency and safety in industrial robotics. Emphasize the importance of efficiency and safety in industrial robotics, underscoring the critical role in improving productivity, reducing costs, and ensuring the well-being of workers. The results were analyzed inline with the four basic robot tasks' completion time. The error rates, collision risks, ergonomic strain, and overall productivity were determined. From the evaluation of the time required to complete each task in the context of human-robot interaction and optimize the workflow to enhance productivity and safety, it was observed that the average task completion time for task 1 was 10.63 minutes; while task 4 was 16.39 minutes. This indicates that some critical tasks require additional resources or adjustments to streamline the process and improve overall performance. In the determination of the safety rating, for each task by subtracting the error rate from 100% performance, it was observed that task 2 has a safety rating of 98% while task 3 has a safety rating of 92%, this indicates that the safety rating is different with respect to a different task. The productivity metrics for each task were determined by multiplying the task completion rate by the task efficiency rate. Hence, the Average Productivity Rating was 0.7449 which indicate the overall productivity in the role of human-robot interaction in enhancing efficiency and safety in industrial robotics. The integration of human-robot interaction in industrial robotics offers a synergistic approach to improving efficiency and safety in manufacturing environments.

Modeling drivers’ dilemma at unsignalized T-intersections under mixed traffic conditions: A case study from India

Objective At unsignalized T-intersections, right turning drivers from major or minor roads have to accept or reject the available gap and cross the intersection. Poor judgment may arise risk of collision with the major road conflicting vehicles. Subject drivers intending to cross the intersection encounter wide range of gaps. Drivers accept large gaps and reject small gaps. However, drivers experience dilemma over a wide range of gaps. This study aims to examine dilemma experienced by the right turning drivers by modeling gap acceptance and rejection decisions for estimating dilemma zone boundaries on major road. Methods Videographic method was considered for collecting traffic movement data at unsignalized T-intersections. Traffic video data was collected at three specific unsignalized T-intersections with varying degree of channelization. After this, accepted and rejected gaps with different traffic characteristics of offending vehicles (subject drivers) and conflicting vehicles such as speed, distance, vehicle type, etc. for right turning movements were extracted from the video data to analyze gap acceptance and rejection decisions of the drivers and estimate dilemma zone boundaries. Results Gap decisions (acceptance or rejection) were modeled using Generalized Linear Model (GLM) as a function of conflicting vehicle type, speed, and distance from the intersection along with degree of channelization for major and minor roads. The results showed that gap rejection probability increased with increment in conflicting vehicle’s speed and decrement in conflicting vehicle’s distance from the intersection. Dilemma zone boundaries were obtained using developed GLM models by estimating the conflicting vehicle distance from the intersection for 90% and 10% gap rejection probabilities. Dilemma zone boundaries were observed to shift farther from the intersection with increase in vehicle speed and vehicle size. The analysis revealed that subject drivers experienced more dilemma while accepting a gap from major road as compared to minor road. Conclusions This study showed that channelization plays a major role in mitigating dilemma of the subject drivers. Overall, the study identified dilemma zone boundaries for unsignalized T-intersections which may assist the right turning subject drivers to cross the intersection safely.