Research on sound quality prediction of vehicle interior noise using the human-ear physiological model.

In order to improve the prediction accuracy of the sound quality of vehicle interior noise, a novel sound quality prediction model was proposed based on the physiological response predicted metrics, i.e., loudness, sharpness, and roughness. First, a human-ear sound transmission model was constructed by combining the outer and middle ear finite element model with the cochlear transmission line model. This model converted external input noise into cochlear basilar membrane response. Second, the physiological perception models of loudness, sharpness, and roughness were constructed by transforming the basilar membrane response into sound perception related to neuronal firing. Finally, taking the calculated loudness, sharpness, and roughness of the physiological model and the subjective evaluation values of vehicle interior noise as the parameters, a sound quality prediction model was constructed by TabNet model. The results demonstrate that the loudness, sharpness, and roughness computed by the human-ear physiological model exhibit a stronger correlation with the subjective evaluation of sound quality annoyance compared to traditional psychoacoustic parameters. Furthermore, the average error percentage of sound quality prediction based on the physiological model is only 3.81%, which is lower than that based on traditional psychoacoustic parameters.

Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis.

Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning-Broken-Down Vehicle" and "Dangerous Situation-Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.

On-Road Evaluation of Unobtrusive In-Car Respiration Monitoring.

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver's seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the feasibility of unobtrusive in-car measurements over long periods of time. Operation of the sensor was investigated over 12 participants sitting in the driver seat. A total of 107 min of driving in diverse conditions with overall coverage rate of 84.45% underscores the sensor potential to reliably capture physiological changes in breathing rate for fatigue and stress detection.

Concentrations of potentially endocrine disrupting chemicals in car cabin air and dust - Effect of temperature and ventilation.

Materials in car cabins contain performance-enhancing semi-volatile organic compounds (SVOCs). As these SVOCs are not chemically bound to the materials, they can emit from the materials at slow rates to the surrounding, causing human exposure. This study aimed at increasing the understanding on abundance of SVOCs in car cabins by studying 18 potential endocrine disrupting chemicals in car cabin air (gas phase and airborne particles) and dust. We also studied how levels of these chemicals varied by temperature inside the car cabin along with ventilation settings, relevant to human exposure. A positive correlation was observed between temperature and SVOC concentration in both the gas and the particle phase, where average gas phase levels at 80 °C were a factor of 18-16,000 higher than average levels at 25 °C, while average particle phase levels were a factor of 4.6-40,000 higher for the studied substances. This study also showed that levels were below the limit of detection for several SVOCs during realistic driving conditions, i.e., with the ventilation activated. To limit human exposure to SVOCs in car cabins, it is recommended to ventilate a warm car before entering and have the ventilation on during driving, as both temperature and ventilation have a significant impact on SVOC levels.

Wait or Pass? Promoting intersection's cooperation via identifying vehicle's social behavior.

Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers' decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants' final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction.

Safety assessment for autonomous vehicles: A reference driver model for highway merging scenarios.

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations.

Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data.

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage.

Evaluation of airborne asbestos concentrations associated with the maintenance of brakes on an industrial overhead crane.

OBJECTIVES: To evaluate potential airborne asbestos exposures during brake maintenance and repair activities on a P&H overhead crane, and during subsequent handling of the mechanic's clothing. METHODS: Personal (n = 27) and area (n = 61) airborne fiber concentrations were measured during brake tests, removal, hand sanding, compressed air use, removal and reattachment of chrysotile-containing brake linings, and reinstallation of the brake linings. The mechanic's clothing was used to measure potential exposure during clothes handling. RESULTS: All brake linings contained between 19.9% to 52.4% chrysotile asbestos. No amphibole fibers were detected in any bulk or airborne samples. The average full-shift airborne chrysotile concentration was 0.035 f/cc (PCM-equivalent asbestos-specific fibers, or PCME). Average task-based personal air samples collected during brake maintenance, sanding, compressed air use, and brake lining removal tasks ranged from 0 to 0.48 f/cc (PCME). The calculated 30-minute time-weighted average (TWA) airborne chrysotile concentration associated with 5-15 minutes of clothes handling was 0-0.035 f/cc PCME. CONCLUSION: The results indicated that personal and area TWA fiber concentrations measured during all crane brake maintenance and clothes handling tasks were below the current OSHA 8-h TWA Permissible Exposure Limit for asbestos of 0.1 f/cc. Further, no airborne asbestos fibers were measured during routine brake maintenance tasks following the manufacturer's maintenance manual procedures. All short-term airborne chrysotile concentrations measured during non-routine tasks were below the current 30-minute OSHA excursion limit for asbestos of 1 f/cc. This study adds to the available data regarding chrysotile exposure potential during maintenance on overhead cranes.

[ADAS and automation-a relevant contribution to maintaining mobility of older drivers?] / Fahrerassistenzsysteme und automatisiertes Fahren ­ ein relevanter Beitrag zum Erhalt der Mobilität im Alter?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have a high level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be a priority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in a critical situation.

The impact of vision loss on attitudes toward autonomous vehicles: A vision-centric analysis.

SIGNIFICANCE: Autonomous vehicles (AVs) have the promise to be an alternative transportation solution for those with vision loss. However, the impact of vision loss on the perceptions and concerns of AVs is unknown. This study therefore examined whether AVs are perceived differently by blind, visually impaired (VI), and normally sighted people. PURPOSE: This study compared the perceptions of AVs among the blind, VI, and normally sighted. METHODS: Participants' opinions on four perception measures (general opinion, trust, impact on quality of life, and intention to use AVs) and nine concerns regarding AVs were measured. The survey was administered to 51 normally sighted, 68 VI, and 65 blind participants. Analyses of covariance assessed whether the four perception measures and nine concerns varied by vision status (normal vision, VI, blind) and driving status (driver, nondriver). Univariate correlations and multiple regression analyses identified associations and predictors of AV perceptions and concerns from demographic, mood, cognition, travel behavior, and vision measures, which included visual acuity, contrast sensitivity, and visual field. RESULTS: The blind (p<0.001), VI (p<0.001), and nondrivers (p<0.001) showed a greater intention to use AVs compared with those with normal vision and drivers. Similar findings were found for the other perception measures. As visual acuity, contrast sensitivity, and visual field extent declined, positivity toward AVs increased (p<0.001). Visual field extent best predicted general opinion and trust in AVs, whereas driving measures were the best predictors of impact on quality of life and intention to use AVs. Concerns about AVs showed no differences based on vision (p=0.94) or driving (p=0.63) status. CONCLUSIONS: Individuals with vision loss expressed more acceptance of AVs despite their concerns. How positive someone is toward AVs appears to be dependent on their visual field extent and driving status.

Chapitre 3. Identité décentralisée et biométrie dans l'automobile.

The car manufacturers continue their offer of mobility services around a customer who is no longer only owner of a vehicle but also simple temporary user. To improve the customer experience, we need to identify the real driver by using decentralized identity on the blockchain, coupled with a biometric system.In this article, based on the experience of a concrete project, we have evaluated the several biometrical methods for capturing information and their reliability in the automotive industry. We will share the lesson learned and the remaining tasks. This elegant means of identifying and exchanging data across customer journeys will open new opportunities between stakeholders. This collaborative co-creation will constitute a digital transformation in the interactions within an ecosystem.

Materials Challenges in the Electric Vehicle Transition.

The ongoing transition toward electric vehicles (EVs) is changing materials used for vehicle production, of which the consequences for the environmental performance of EVs are not well understood and managed. We demonstrate that electrification coupled with lightweighting of automobiles will lead to significant changes in the industry's demand not only for battery materials but also for other materials used throughout the entire vehicle. Given the automotive industry's substantial consumption of raw materials, changes in its material demands are expected to trigger volatilities in material prices, consequently impacting the material composition and attractiveness of EVs. In addition, the materials recovered during end-of-life recycling of EVs as the vehicle fleet turns over will impact recycled material supplies both positively and negatively, impacting material availabilities and the economic incentive to engage in recycling. These supply chain impacts will influence material usage and the associated environmental performance of not only the automotive sector but also other metal-heavy industries such as construction. In light of these challenges, we propose the need for new research to understand the dynamic materials impacts of the EV transition that encompasses its implications on EV adoption and fleet life cycle environmental performance. Effectively coordinating the coevolution of material supply chains is crucial for making the sustainable transition to EVs a reality.

Comparative analysis of real-world vehicular emissions from BS-IV and BS-VI cars in India.

This study investigates real-world carbon dioxides (CO2) and nitrogen oxides (NOx) emissions from diesel (Bharat Stage-IV (BS-IV)) and petrol/gasoline (BS-IV and BS-VI) cars in Indian driving conditions using a portable emission measurement system (PEMS). The paired sample t-test revealed a significant difference ( p < 0.05) in NOx and CO2 emissions among the three types of cars, except for CO2 emissions ( p > 0.05) between BS-IV petrol and BS-VI petrol cars. The highest NOx emission rates were observed in all car types during acceleration (> 1 m/s2) and deceleration (- 2 m/s2). CO2 emission rates were also high during acceleration (> 1 m/s2) for all car types. At low speeds (around 20 kmph), all car types had low emissions of CO2 and NOx, with acceleration and deceleration rates ranging from - 0.5 to 0.5 m/s2. BS-IV diesel cars emit significantly higher NOx emissions compared to petrol cars, especially at vehicle-specific power (VSP) bin 0 (deceleration to idling mode) and during VSP bin 7 (acceleration mode). BS-IV diesel cars emit 228% and 530% higher NOx emissions than BS-IV and BS-VI petrol cars at VSP bins 0 and 7, respectively. CO2 emissions from BS-VI petrol cars were 10% lower than those from BS-IV petrol cars across all VSP bins, indicating moderate reductions. Furthermore, diesel cars emit 140% less CO2 emissions than petrol cars across various VSP bins. The findings highlight the need for cleaner technologies and responsible driving practices to address vehicular emission concerns.

Drinking and driving: A systematic review of the impacts of alcohol consumption on manual and automated driving performance.

INTRODUCTION: Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving. METHOD: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model. RESULTS: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance. CONCLUSIONS: Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems. PRACTICAL APPLICATIONS: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems.

Perceptions of vulnerable roadway users on autonomous vehicle regulations.

INTRODUCTION: Development and implementation of autonomous vehicle (AV) related regulations are necessary to ensure safe AV deployment and wide acceptance among all roadway users. Assessment of vulnerable roadway users' perceptions on AV regulations could inform policymakers the development of appropriate AV regulations that facilitate the safety of diverse users in a multimodal transportation system. METHOD: This research evaluated pedestrians' and bicyclists' perceptions on six AV regulations (i.e., capping AV speed limit, operating AV in manual mode in the sensitive areas, having both pilot and co-pilot while operating AVs, and three data-sharing regulations). In addition, pedestrians' and bicyclists' perceptions of testing AVs in public streets were evaluated. Statistical testing and modeling techniques were applied to accomplish the research objectives. RESULTS: Compared to the other AV regulations assessed in this research, strong support for AV-related data sharing regulations was identified. Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. CONCLUSIONS: Policymakers and AV technology developers could develop effective educational tools/resources to inform pedestrians and bicyclists about AV technology reliability and soften their stance, especially on AV regulations, which could delay technology development. PRACTICAL APPLICATIONS: The findings of this research could be used to develop informed AV regulations and develop policies that could improve pedestrians' and bicyclists' attitudes/perceptions on regulating AVs and promoting AV technology deployments.

Factors Affecting the Situational Awareness of Armored Vehicle Occupants.

In the field of armored vehicles, up to 70% of accidents are associated with low levels of situational awareness among the occupants, highlighting the importance of situational awareness in improving task performance. In this study, we explored the mechanisms influencing situational awareness by simulating an armored vehicle driving platform with 14 levels of experimentation in terms of five factors: experience, expectations, attention, the cueing channel, and automation. The experimental data included SART and SAGAT questionnaire scores, eye movement indicators, and electrocardiographic and electrodermal signals. Data processing and analysis revealed the following conclusions: (1) Experienced operators have higher levels of situational awareness. (2) Operators with certain expectations have lower levels of situational awareness. (3) Situational awareness levels are negatively correlated with information importance affiliations and the frequency of anomalous information in non-primary tasks. (4) Dual-channel cues lead to higher levels of situational awareness than single-channel cues. (5) Operators' situational awareness is lower at high automation levels.

Law compliance decision making for autonomous vehicles on highways.

As autonomous driving advances, autonomous vehicles will share the road with human drivers. This requires autonomous vehicles to adhere to human traffic laws under safe conditions. Simultaneously, when confronted with dangerous situations, autonomous driving should also possess the capability to deviate from traffic laws to ensure safety. However, current autonomous vehicles primarily prioritize safety and collision avoidance in their decision-making and planning. This may lead to misunderstandings and distrust from human drivers in mixed traffic flow, and even accidents. To address this, this paper proposes a decoupled hierarchical framework for compliance safety decision-making. The framework primarily consists of two layers: the decision-making layer and the motion planning layer. In the decision-making layer, a candidate behavior set is constructed based on the scenario, and a dual layer admission assessment is utilized to filter out unsafe and non-compliant behaviors from the candidate sets. Subsequently, the optimal behavior is selected as the decision behavior according to the designed evaluation metrics. The decision-making layer ensures that the vehicle can meet lane safety requirements and comply with static traffic laws. In the motion planning layer, the surrounding vehicles and the road are modeled as safety potential fields and traffic laws potential fields. Combining the optimal decision behavior, they are incorporated into the cost function of the model predictive control to achieve compliant and safe trajectory planning. The planning layer ensures that the vehicle meets trajectory safety requirements and complies with dynamic traffic laws under safe conditions. Finally, four typical scenarios are used to evaluate the effectiveness of the proposed method. The results indicate that the proposed method can ensure compliance in safe conditions while also temporarily deviating from traffic laws in emergency situations to ensure safety.

The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy.

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.

Mechanism of PGMs capture from spent automobile catalyst by copper from waste printed circuit boards with simultaneous pollutants transformation.

The traditional pyrometallurgical recycling of nano-sized platinum group metals (PGMs) from spent automotive catalysts (SACs) is an energy-intensive process that requires the addition of large quantities of copper capture and slag-forming reagents. Similarly, pyro-recycling of valuable metals from waste printed circuit boards (WPCBs) is also an energy- and reagent-intensive process that and carries a risk of pollution emissions. Based on the complementarity of composition and similarity of recycling process, synergistic pyro-recycling of SACs and WPCBs allow copper in WPCBs to capture PGMs in SACs and oxides from two waste form slag jointly, which offers benefits of enhanced metal recovery, reduced reagent and energy consumption, and suppressed pollutant emissions. However, the mechanisms of PGMs capture and pollutant transformation in co-smelting remain unknown. Here, we investigated the sub-processes mechanisms of slag formation, brominates fixation, multi-metal distribution and kinetic settlement. Oxides in both wastes support SiO2-Al2O3-CaO slag formation with low melting point and viscosity, where CaO suppresses the emission of brominated pollutants. Copper (50-100 µm) from WPCBs facilitates nano-sized PGMs in SACs recovery through capture and settlement. The results of demonstration experiments indicated a recovery rate of 94.6 %, 96.8 %, 97.2 %, and 98.1 % for Cu, Pt, Pd, and Rh, respectively, with a debromination efficiency exceeding 98 %. The theoretical analysis provides support for the establishment of a synergistic pyro-recycling process for SACs and WPCBs and provides insights into the potential for a greener and more efficient co-recycling of multi urban mines.

Vehicle Lane-Changing scenario generation using time-series generative adversarial networks with an Adaptative parameter optimization strategy.

Connected and automated vehicles (CAVs) hold promise for enhancing transportation safety and efficiency. However, their large-scale deployment necessitates rigorous testing across diverse driving scenarios to ensure safety performance. In order to address two challenges of test scenario diversity and comprehensive evaluation, this study proposes a vehicle lane-changing scenario generation method based on a time-series generative adversarial network (TimeGAN) with an adaptive parameter optimization strategy (APOS). With just 13.3% of parameter combinations tested, we successfully trained a satisfactory TimeGAN and generate a substantial number of lane-changing scenarios. Then, the generated scenarios were evaluated for diversity, fidelity, and utility, demonstrating their effectiveness in capturing a wide range of driving situations. Furthermore, we employed a Lane-Changing Risk Index (LCRI) to identify the rare adversarial cases in scenarios. Compared to real scenarios, our approach generates 27 times more adversarial cases with 1.8 times higher average risk, highlighting its potential for uncovering critical safety vulnerabilities. This study paves the way for more comprehensive and effective CAV testing, ultimately contributing to safer and more reliable autonomous driving technologies.