Collision Risk Research Articles

Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers’ overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.

Assessing the susceptibility of raptor species to electrocution: A framework for Kenya

Developing countries are witnessing rapid expansion of electrical infrastructure to meet increasing energy demands, prompting a critical need to assess the potential impact on avian biodiversity. Leveraging open access data, our study conducted a comprehensive assessment to detect electrocution and collision risk hotspots in Kenya while identifying raptor species highly susceptible to these risks. Through an integrated approach that considers morphological and behavioral traits of the species, environmental factors at the site, and technical parameters of the power lines, we developed risk maps and categorized raptors’ susceptibility into high, medium, and low-risk levels. Applying this framework, we identified three raptor species at high risk of electrocution; the long-crested eagle, the augur buzzard, and the steppe eagle. Meru and Kiambu counties emerged as high-risk areas due to substantial overlap between high-risk buffer zones and areas with high raptor species distribution. It is worth noting that this framework only accounts for thirteen raptor species, and there is scope to expand it in the future to include other avian species, such as storks, bustards, pelicans and flamingos, which are also susceptible to electrocution and collision incidents and measures to mitigate electrocution of raptors may not be sufficient for these other group of birds.

Reduced survival in a soaring bird breeding in wind turbine proximity along the northern Baltic Sea coast

Wind power plays a vital role in global climate action and plans for new turbines in the Baltic Sea region are underway. However, it is crucial not to overlook the environmental impact on wildlife, which can be difficult to quantify, especially in elusive and hard-to-track species. One of these species is the white-tailed eagle, a conservation success story that faces a significant collision risk with rotor blades, particularly as turbines are being constructed in its coastal habitats. To assess the effect of wind turbines on survival of territorial adults, we genotyped DNA from adult feathers collected at nests between 2010 and 2022. By tracking individuals across years, we measured survival and breeding dispersal in relation to wind turbine presence. Turbines within a 5 km radius of nests were found to reduce annual survival rates by 7.6 %, while resighting probability and breeding dispersal probability were unaffected. The proportion of territories exposed to wind turbines is currently low (4–5 %), mainly because recent construction sites have been further inland. However, future projections suggest an increase in territory exposure, indicating potential for population-level risks. This highlights the need for substantial safety buffers around nest sites (preferably >5 km) to protect the breeding white-tailed eagle population. Currently, a comprehensive understanding of the large-scale impact on the Baltic Sea white-tailed eagle population is lacking. As wind power expands, it is necessary to consider its impact on wildlife and we recommend conducting ongoing environmental assessments to monitor and adapt conservation measures.

Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.50% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.

Safe Maneuvering Planning for Flights in Complex Environments

Safe trajectory planning of unmanned aerial vehicles (UAVs) has drawn significant attention. For avoiding obstacles, the two prevailing methods of distance penalty and convex polyhedra confinement ignore the flight velocity, aggressive maneuvering increases the risk of collision. Other approaches ensure safety by limiting the flight speed, leading to conservative and slow trajectories. This paper presents a motion planning method that ensures flexible maneuverability and a high level of UAV safety in unknown complex environments. Firstly, an improved path planning method considering safety and search efficiency is proposed to generate discrete primitives. By representing the confident flight area with ball-shaped areas expected no obstacles included, the safe region constraint is designed and incorporated into the first-stage optimization with the low-order kinematic model. Subsequently, an agile maneuvering approach with environmental adaptability is integrated into the second-stage optimization with the high-order kinematic model. This approach can effectively adjust the agile flight according to the obstacle distribution and motion velocity. Simulations and real-world experiments are conducted to demonstrate the robustness and effectiveness of the presented approach. Moreover, benchmark comparison manifests that the proposed method outperforms the cutting-edge methods from the aspects of the perception uncertainty adaptation and flight safety.

A novel STPA approach to software safety and security in autonomous maritime systems

With higher autonomy in maritime systems, tasks and responsibilities are moved from the human operator to software, increasing the complexity and the importance of safe and reliable functionality. Software failures, however, may be introduced from the early life cycle phases intentionally or unintentionally, and these must therefore be mitigated by safe and secure design approaches. A challenge is that existing methods are not particularly well-suited for analyzing software risks. Thus, the objective of this paper is to propose a systematic and efficient software failure identification approach by extending the Systems-Theoretic Process Analysis (STPA) with a software failure taxonomy and the System Modeling Language (SysML). This enables the control structure in STPA to cover both the dynamic and static aspects of the software functions. Combined with an implementation platform independent questionnaire, this gives a more systematic and guided search for potential software failures than existing approaches. To demonstrate the proposed approach, a case study on a ferry's navigation system that operates in manual control or semi-autonomous mode is performed. In the case study, the focus is on creating an avoidance map data structure, including both moving and static obstacles to be avoided by the ferry, and the subsequent process of collision risk warning calculation. Software failures are identified and evaluated in collision scenarios where the ferry operates under foggy conditions. The paper shows that the proposed systematic approach provides an improved process for identifying and analyzing critical software failures. This facilitates enhanced risk mitigation in the design and testing phases contributing to autonomous systems' safety and security.

Crossings and collisions – Exploring how roe deer navigate the road network

ContextTo investigate the major impact of roads on wildlife, most studies focus on hot-spots of wildlife-vehicle collisions (WVC) to identify areas in need of mitigation measures. However, on road stretches where the frequency of WVC is low, a question arises: is this because those locations are 'safe’ places for wildlife to cross the road with little risk of collisions; or is it because individuals avoid approaching and crossing the road in these locations?ObjectivesIn this study, we addressed this gap by evaluating how roe deer crossings are related to WVC risk across the road network.MethodsWe used 56 076 WVC locations between 2013 and 2017 to predict the spatiotemporal risk zones in response to environmental, road-related and seasonal predictors using Species-Distribution Modelling (SDM). We compared the predictive WVC risk to the location of 20 744 road crossing by 46 GPS-collared roe deer individuals.ResultsWe found that the risk of WVC with roe deer tends to be higher on federal roads that are present in a density of approximate 2.2 km/km2 and surrounded by broad-leafed forests and demonstrate that SDMs can be a powerful tool to predict the risk of WVC across the road network. Roe deer crossed roads more frequently in high WVC risk areas. Temporally, the number of WVC changed throughout the year, which can be linked to roe deer movement patterns rather than landscape features. Within this study, we did not identify any road segments that were a complete barrier to roe deer movement.ConclusionsThe absence of complete barriers to roe deer movement detected in the present study, might be due to the low spatial variation of the landscape, coupled with the high individual variation in movement behaviour. By applying our approach at greater spatial scales and in other landscape contexts, future studies can continue to explore the potential barrier impacts of roads on landscape connectivity. Exploring the relationship between crossing activity and collision risk can improve one’s ability to correctly identify road stretches that require mitigation measures to improve connectivity versus reduce collisions.

Railway Train Collision Avoidance on Same track and Animal Detection Using AI - IoT

The increasing demand for safer and more efficient railway transportation systems has prompted the exploration of advanced technologies to mitigate collision risks and address emerging challenges such as animal incursions on railway tracks. This paper proposes a comprehensive solution leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies for real-time detection and avoidance of collisions between trains operating on the same track and encounters with animals. These sensors capture various environmental and operational data such as train positions, velocities, and track conditions. The AI algorithms analyze this data to identify potential collision risks and animal intrusions. Additionally, real-time communication between trains and the centralized control system enables timely intervention and rerouting decisions to ensure safe operations. The system incorporates advanced image recognition algorithms to detect and classify animals near railway tracks. Utilizing high-resolution cameras and IoT-connected devices, the system identifies animals in the vicinity and alerts train operators or initiates automated braking mechanisms to prevent accidents caused by animal incursions. Key features of the proposed system include scalability to accommodate varying railway infrastructures, adaptability to diverse environmental conditions, and interoperability with existing railway signaling and control systems. Moreover, the integration of AI and IoT technologies enhances the system's predictive capabilities, enabling proactive risk mitigation and improving overall.

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management.

Automatic Identification of Emergency Sounds for Self-Driving Cars

As the development of Self-Driving Cars (SDCs) advances, one important feature that requires attention is their ability to sense and respond to emergencies on the road. Drivers of emergency vehicles prioritize speed over safety during emergencies to save lives, potentially leading to accidents. However, manual cars sometimes may not understand the urgency of the situation, further increasing the risk of collisions. In such cases, self-driving cars offer a better solution to replace manual cars and minimize road accidents. These cars with emergency sound detections offer a level of responsiveness, accuracy, and consistency that surpasses manual cars, contributing to improved road safety and efficiency in emergencies. Therefore, the key objective of the research is to improve the listening capability of self-driving cars to detect emergency vehicles with the help of a hybrid feature extraction technique. The suggested technique leverages a combination of Complex Morlet Wavelet and Co-occurrence Matrix to obtain statistical features from the emergency sounds. The proposed technique can work with the input length of 1.2 seconds of raw waveforms. This research work investigates that self-driving cars can accurately identify emergency vehicles by examining the distinctive emergency sound patterns emitted by the emergency vehicle with the highest accuracy of 94%. At the same time, the proposed technique reduces the computational cost by 20 – 40 milliseconds when compared with other techniques. The result of this work not only provides better accuracy but also reduces detection time, which is a crucial requirement for real-time applications such as self-driving cars.

International Law Review Of Space Debris Mitigation Efforts

Space debris has become an increasingly pressing issue in space exploration and utilization. This article conducts a comprehensive review of aspects of international law relating to space debris mitigation efforts. This research analyzes existing legal frameworks, including relevant multilateral treaties and international legal principles related to the space environment. With a focus on the principles of shared responsibility and avoidance of damage to the space environment, the study identifies key challenges in managing space junk and reducing the risk of collisions in earth orbit. An in-depth analysis of the role of national space agencies and international organizations in implementing mitigation measures was also carried out. In the context of international cooperation, this article evaluates existing collaborative efforts and proposes new measures to improve the effectiveness of space debris mitigation. A better understanding of legal responsibility and burden-sharing in addressing these issues became a central point in the study. By analyzing recent developments in space law and international environmental regulations, this article provides insight into the direction the global community might take in addressing future space debris mitigation challenges. It is hoped that this research can provide guidance for policymakers, academics, and legal practitioners in their efforts to maintain a clean and sustainable space environment.

COLREGs Misunderstood or Ignored?

As various scholarly articles and accident investigation reports indicate, the risk of collisions at sea is still high. The objective of this research is to identify if this is due to misunderstanding of the International Regulations for Preventing Collisions at Sea 1972, as amended (COLREGs), by watchkeeping officers working onboard cargo ships. A total of 76 collisions while navigating officers (OOW) were at the con that occurred during a period of 10 years were analysed.80% of the collisions could have been avoided since one vessel was aware of the presence of the other. The highest number of collisions had taken place in cases of crossing situations. Furthermore, it is also important to note that 52% of the OOWs involved in collisions were sufficiently experienced.No evidence was found to conclude that these accidents occurred due to misinterpretation or misunderstanding of the COLREG’s by the OOW’s onboard the cargo ships, but as incorrect or insufficient action had been taken by experienced OOW’s, this could have been due to complacency and/or overconfidence on their part.Among the measures to be taken to minimize collisions at sea, overconfidence and complacency are two factors that should also require attention by all interested parties.

The impact of the presence and utilization policy of a dedicated lane on drivers’ preference to use automation and driving behaviour on motorways

Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers’ preference to use automation and their behaviour has not yet been thoroughly investigated.In this study, a driving simulator experiment is conducted, where participants drive a CAV in the presence of a DL with different utilization policies. Drivers have the possibility to choose between driving in an automated mode or in a manual mode. In automated mode they could adjust the driving speed and time headway and initiate automated lane changes. Two utilization policies were examined: mandatory versus optional use of DLs when driving in an automated mode. The impact of the presence and utilization policy of the DL on drivers’ preference to use automation and their behaviour in car-following and lane changing are investigated.The study found that while the presence of a DL does not increase drivers’ preference for automation use, it encourages drivers to utilize the DL more when the utilization policy is mandatory (i.e., drivers can only use automation mode when driving on this lane). Furthermore, drivers are more conservative in automated mode and when driving in mixed traffic. However, they perform closer car-following and merge into smaller gaps when driving on DLs which on one hand can increase the capacity of the DLs, but on the other hand can increase the risk of collisions. These results are useful for road operators, and in setting-up a more realistically traffic simulation studies.

Autonomous thermal tracking reveals spatiotemporal patterns of seabird activity relevant to interactions with floating offshore wind facilities

Planning is underway for placement of infrastructure needed to begin offshore wind (OSW) energy generation along the West Coast of the United States and elsewhere in the Pacific Ocean. In contrast to the primarily nearshore windfarms currently in the North Atlantic, the seabird communities inhabiting Pacific Wind Energy Areas (WEAs) include significant populations of species that fly by dynamic soaring, a behavior dependent on wind and in which flight height increases steeply with wind speed. Therefore, a more precise and detailed assessment of their 3D airspace use is needed to better understand the potential collision risks that OSW turbines may present to these seabirds. Toward this end, a novel technology called the ThermalTracker-3D (TT3D), which uses thermal imaging and stereo vision, was developed to render high-resolution (on average within ±5 m) flight tracks and related behavior of seabirds. The technology was developed and deployed on a wind-profiling LiDAR buoy in the Humboldt WEA, located 34 to 57 km off California’s coast. During the at-sea deployment between 24 May and 13 August 2021, the TT3D successfully tracked birds moving between 10 and 500 m from the device, around the clock, and in all weather conditions; a total of 1407 detections and their corresponding 3D flight trajectories were recorded. Mean altitudes of detections ranged 6-295 m above sea level (asl). Considering the degree of overlap with anticipated rotor swept zones (RSZ), which extend 25-260 m asl, 79% of detected birds (per m3 of airspace) moved below the RSZ, 21% moved at heights overlapping the RSZ, and another 0.04% occurred at heights exceeding the RSZ. The high-resolution tracks provided valuable insight into seabird space use, especially at heights that make them vulnerable to collision during various environmental conditions (e.g., darkness, strong winds). Observations made by the TT3D will be useful in filling critical knowledge gaps related to estimating collision and avoidance between seabirds and OSW facilities in the Pacific and elsewhere. Future research will focus on enhancing the TT3D’s identification capabilities to the lowest taxon through validation studies and artificial intelligence, further contributing to seabird conservation efforts associated with OSW.

Evolutionary model and risk analysis of ship collision accidents based on complex networks and DEMATEL

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships.

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

Advanced Driver Assistance System (ADAS)

The motivations are art, elegance, and ethics. Human error and negligence cause 90% of road accidents, making the human the important part in this process while also being the weakest link. Every year, numerous accidents are reported as a result of excessive speed and poor decision-making. According to a logical concept, every 1% increase in velocity increases the probability of a fatal collision by 4% and the risk of a serious collision by 3%. Modern automobiles are a symbiosis of several electrical subsystems that work together to provide a safe and enjoyable driving experience. One of the technologies used in automotive development is the Advanced Driver Assistant System (ADAS). It is a system that monitors different parameters related to the vehicle and its surroundings in order to detect potentially dangerous circumstances early on. Sensors with sophisticated electronics, known as Sensor Network (SN), are already installed in many cars today to accomplish (ADAS) technology. A well-known ADAS is the Adaptive Speed Control system (ASC), Automatic Brake System (ABS), Warning Collision System (WCS), and Legal Restriction System, which are used to avert high-speed accidents. In addition, there are Lane Keeping Systems (LKS) and Lane Change Systems (LCS).

Risk Analysis and Visualization of Merchant and Fishing Vessel Collisions in Coastal Waters: A Case Study of Fujian Coastal Area

The invasion of ship domains stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying the collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validates it through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of the collision risk. Finally, taking the high-risk area for merchant and fishing vessel collisions in the Minjiang River Estuary as an example, this paper conducts an analysis that involves classifying ship collision scenarios, extracts risk data under different collision scenarios, and visually analyzes areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis are verified by the number of accidents that occurred in the most recent three years.

Exploration of COLREG-relevant parameters from historical AIS-data

Abstract Reliable anti-collision control algorithms conforming with the rules regulating traffic at sea, the International Regulations for Preventing Collisions at Sea (COLREG), are essential for the deployment of autonomous vessels in waters shared with other ships. The development of such methods is an active field of research. However, little attention has been given to how these rules are interpreted by experienced mariners, and how such information can be parametrised for use in automatic control systems and autonomous ships. This paper presents a method for exploiting historical automatic identification system (AIS) data to characterise parameters indicating the prevalent practices at sea in encounters with high collision risk. The method has been tested on data gathered in areas off the Norwegian coast over several years. Statistics on relevant parameters from the resulting dataset and the relation between them is presented. The results indicate that the strongest influence on vessel behaviour is the type of situation, and the amount of land and grounding hazards in the vessel's proximity.

Carbon fiber-sampling combined flame ionization mass spectrometry for direct analysis of drugs in oral fluid

Drug-impaired driving poses a significant risk of collisions and other hazardous accidents, emphasizing the urgent need for simple and rapid roadside detection methods. Oral fluid, as an easily collectible and non-invasive test material, has gained widespread use in detecting drug-impaired driving. In this study, we have devised a method for direct sampling using a carbon fiber bundle combined with flame ionization mass spectrometry. The essence of this method lies in the synergy between the adsorption properties of carbon fiber and the plasma characteristics of the flame. Leveraging the strong adsorption capabilities of the carbon fiber bundle allows for the use of a minimal sample size (<100 μL) during sampling, presenting a distinct advantage in the roadside inspection and sampling process. Throughout the flame ionization process, proteins and salts within the oral fluid matrix adhere well to the carbon fiber bundle, while small molecule targets can be efficiently desorbed and react with charged species in the flame, leading to ionization. The results demonstrate the successful development of carbon fiber-sampling combined flame ionization mass spectrometry, capable of qualitative and quantitative analysis of drugs in oral fluid without the need for sample pre-treatment. Its quantitative capabilities are sufficient for real sample detection, providing an effective analytical method for the roadside detection of drugs in oral fluids.