Road Test Research Articles

Developing Design-Basis PSD Loads for Spent Nuclear Fuel Transport: Insights from Cask Test Results

From 2020 to 2021, multimodal transportation tests were conducted in South Korea. Surrogate fuel assemblies were loaded into a full-sized transportation cask for road and sea transportation tests at Doosan Enerbility and along a South Korean seashore. The study focused on the effects of normal transport conditions on spent nuclear fuel (SNF) and its structural integrity by analyzing the data obtained. Additionally, the need to analytically assess the integrity of spent fuel under these conditions through design-basis loads has emerged. This study focuses on generating the input load. The study also addressed the practical considerations for applicants regarding the format of these design loads. For all tested conditions, design-basis power spectral density (PSD) loads were identified, covering PSDs at the basket across various transportation conditions. A comprehensive diagram capturing the PSDs at the basket, which then transmitted the load to the fuel assembly for each transport scenario, was calculated as the design load. The transportation test load data served as inputs for the shake table test, which aimed to closely evaluate the behavior of the fuel assemblies. The appropriateness and conservativeness of the derived design-basis PSD were validated through a shake table tests.

Low-dose cement stabilized large particle size graded crushed stone: Laboratory and field investigations

Compared with traditional graded crushed stone (GCS), the maximum aggregate size of large particle size GCS (LPS-GCS) is larger and the content of large-size aggregates is more, which can form an interlocking skeletal structure with a stronger bearing capacity. Nevertheless, LPS-GCS occasionally exhibits unstable performance, and one effective way to enhance LPS-GCS's mechanical and physical properties is to stabilize it with low-dose cement. In this study, four cement contents (1.5 %, 2.0 %, 2.5 %, and 3.0 %, by mass) were selected to form low-dose cement stabilized LPS-GCS (LCS-LPS-GCS) mixtures with a maximum aggregate particle size of 53 mm. Unconfined compressive strength (UCS) and compressive resilient modulus (CRM) tests were performed on LCS-LPS-GCS samples for different cement contents and curing times. Moreover, prediction models and correlations for the UCS and CRM at different curing times were established. Furthermore, a test road was used to evaluate the LCS-LPS-GCS's field compaction level and deflection. Laboratory test results indicated that while the California bearing ratio was significantly higher than that of conventional GCS, the maximum dry density of LPS-GCS designed in this study was close to that of conventional GCS. With the same cement content, LCS-LPS-GCS outperformed conventional GCS in terms of UCS and CRM. Based on the variation law of the mechanical properties, the ideal cement content should be around 2.5 %. There was a strong positive correlation between UCS and CRM scores from LCS-LPS-GCS. The UCS and CRM were appropriate to be predicted by exponential and growth functions models, respectively. The field investigations indicated that asphalt pavements reconstructed with LCS-LPS-GCS had high compaction and low deflection, resulting in better bearing capacity and durability. The results of this investigation can serve as a reference for the design and application of LCS-LPS-GCS base for long-lasting and resilient pavements.

Vehicle drivability evaluation based on PCA-SSA-XGBoost

In order to improve the efficiency and quality of vehicle drivability evaluation, a drivability evaluation model based on principal component analysis, extreme gradient boosting tree and sparrow optimization algorithm is proposed. In this paper, the power upshift of DCT vehicle is taken as a typical working condition, and 18 objective evaluation indicators under the power upshift working condition are studied and defined. The objective evaluation indicators are simplified by principal component analysis, their redundancy and blockiness are reduced, and the model input samples are optimized. The extreme gradient boosting tree model is trained to predict the subjective score of drivability, and the sparrow algorithm is used to optimize the core hyperparameters of the extreme gradient boosting tree to improve the accuracy and stability of the model. Road tests show that after the objective evaluation indicators are simplified by principal component analysis, the model evaluation accuracy reaches 97%, which is better than BPNN ( 90%), SVM ( 91%), ELM ( 92%) and SSA-XGBoost ( 95%). It is proved that the accuracy and stability of the proposed PCA-SSA-XGBoost model are better than other models, and it can complete the drivability evaluation more effectively. The evaluation model can be transferred and applied to other driving conditions, and has application value in solving the subjective and objective mapping problem in drivability evaluation.

Analysis of Energy Efficiency Parameters of a Hybrid Vehicle Powered by Fuel with a Liquid Catalyst

A notable trend in the modern automotive market is the increased interest in hybrid cars. Hybrid cars combine a standard internal combustion engine with an electric motor solution. Research into increasing the energy efficiency of a conventional unit while meeting increasingly stringent exhaust emission standards is becoming a key postulate in this matter. This article discusses an analysis of modifying the fuel used by hybrid vehicles using the example of a selected drive unit equipped with a spark-ignition engine. This effect was tested after the Eco Fuel Shot liquid catalyst was added to the fuel. The research process was carried out in two stages, as follows: in road conditions using the Dynomet road dynamometer; and on the V-tech VT4/B2 chassis dynamometer. Tests were carried out to replicate road tests with a catalytic additive in the fuel. A mathematical model was created and the following energy efficiency parameters of the hybrid vehicle were calculated: the torque of the internal combustion engine, electric motor, and generator; the rotational speeds of the internal combustion engine, electric motor, and generator; the power of the internal combustion engine, electric motor, and generator; the equivalent fuel consumption of the electric motor and generator; the fuel consumption of the internal combustion engine, electric motor, and generator; and the mileage fuel consumption of the internal combustion engine, electric motor, and generator. The results of the tests made it possible to identify the benefits of using the tested liquid catalyst on the operation of the drive system of the analyzed hybrid vehicle. This research will be of benefit to both the demand side in the form of users of this category of vehicles, and the supply side represented by the manufacturers of power units.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Study on the relationship between high temperature oxidation and temperature rise rate of engine oil

Purpose The purpose of this paper is to study the relationship between high temperature oxidation and temperature rise rate of engine oil attempted to explore a new indicator to evaluate oil degradation. Design/methodology/approach Accelerated oxidation test combined with molecular simulation and road test is carried out in this paper. The temperature rise characteristics of mineral oil and synthetic oil under different oxidation temperatures (140°C, 155°C and 170°C) and time (50 h, 100 h, 150 h and 200 h) were determined by accelerated oxidation. The mechanism of temperature change characteristics of used oils was analyzed with molecular simulation. Two experimental vehicles carried six road tests with synthetic and mineral oil. Findings The results of this study show that the temperature rise rate of oxidized mineral and synthetic oil is higher than the new oil. The temperature rise rate is proportional to the oxidation time and oxidation temperature. The synthetic engine oil temperature rise rate is lower than that of the mineral engine oil. The same result was obtained in road tests. Molecular simulation verifies that small molecules were generated after oil oxidation which results in intermolecular friction and increased heat generation. Originality/value This paper indicates that temperature rise rate has potential to be taken as an indicator to evaluate oil oxidation which provides a new way for engine oil analysis. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0177/

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO2, CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R2: 0.76), while polynomial regression excelled for CO2 (R2: 0.93). For warm engines, polynomial regression performed best for CO2 (R2: 0.95), and gradient boosting delivered results for THC (R2: 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

Development and tests of the 499.8 MHz srf cryomodules for HEPS

A 499.8 MHz single-cell superconducting cavity has been proposed as the active third-harmonic cavity for the High Energy Photon Source (HEPS), a 6 GeV diffraction-limited synchrotron light source currently under construction in Beijing. Two 499.8 MHz cryomodules are required to provide a total of 3.5 MV of rf voltage and 400 kW of beam power. A mechanically enhanced cavity design, based on the original KEKB-type cavity, was developed to meet the specifications of HEPS. Three cryomodules have been assembled and subjected to horizontal tests. A comprehensive clean-assembly procedure was developed, resulting in excellent rf performance maintained from vertical tests to horizontal tests at cryogenic temperatures. The unloaded quality factors of all three modules exceeded 2.45×109 at 1.75 MV, significantly surpassing the design goal. No field emissions were observed throughout the tests. These are the highest-performing 500 MHz modules treated by buffered chemical polishing, also outperforming the average performance of their electropolished counterparts. Following dedicated analyses and multiple road tests, one cryomodule was successfully transported in its entirety from the test platform to the HEPS tunnel, with well-controlled accelerations on delicate components. Vacuum integrity and performance were well maintained. The module has been accelerating electron beams since August 2024 for the HEPS commissioning. This paper presents the design, fabrication, assembly, cryogenic tests, and transportation of the mechanically improved 499.8 MHz single-cell srf cryomodule.

Smart Rumble Strip System to Prevent Over-Height Vehicle Collisions.

Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated signage are commonly installed. Semi-active systems using laser- or infrared-based detection systems in conjunction with visual warnings have been implemented. Nevertheless, some drivers ignore these visual warnings and collisions continue to occur. This paper presents a novel concept for a collision prevention system, which makes use of a series of sensor-activated, motorized rumble strips. These rumble strips span across a certain distance ahead of a low clearance bridge. When an over-height vehicle is detected, a mechanism is triggered which elevates the rumble strips. The noise and vibrations produce a vigorous alert to the offending driver. They also increase effective friction of the road surface, thus assisting to slow down the vehicle and shorten the stopping distance. The strips will be lowered after a certain time has elapsed, thus minimizing their effects on other vehicles. This article presents a conceptual framework and quantifies the vibration and noise caused by rumble strips in road tests. Road tests indicated that the vibration level typically exceeded 1 g and noise level reached approximately 90 dB in the cabin of a 3.5-ton truck. Fabrication of a proof-of-concept mechanized rumble strip model was presented and verified in an outdoor environment. The circuitry and mechanical design, and requirements in actual implementation, are discussed. The proposed event-triggered rumble strip system could significantly mitigate over-height vehicle collisions that cause major disruptions and injuries worldwide. Further works, including a comprehensive road test involving various types of vehicles, are envisaged.

Emissions of gaseous nitrous acid (HONO) from diesel trucks: Insights from real-driving emission experiments

Nitrous acid (HONO) serves as a substantial contributor in the atmospheric chemistry of hydroxyl radicals (·OH). Despite its significance, the primary sources of atmospheric HONO, particularly diesel truck emissions, have not been thoroughly examined. This study investigated the factors influencing HONO emissions via on-road exhaust emission tests using a self-developed portable measurement system. The findings show that the HONO emissions measured during on-road testing are higher than those measured during chassis dynamometer testing, highlighting the need for on-road tests to capture HONO emissions. Emission standards and truck types greatly influence HONO emission factors (EFs), with stricter regulations leading to lower emissions. The average fuel consumption-based EFs for light-duty diesel trucks ranged from 0.93 g/kg for China III to 0.08 g/kg for China VI. For medium-duty diesel trucks, the EFs decrease from 1.43 g/kg for China III to 0.19 g/kg for China V. Moreover, the vehicle-specific power demonstrated a stronger correlation with HONO emissions. This research showed that HONO emissions were significantly higher without or before the optimal operation of the SCR device, and the device notably reduced HONO emissions. Future research should focus on the impact of various exhaust after-treatment systems and explore HONO conversion mechanisms.

Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm

An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.

A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.

Experimental Validation of Truck Cab Suspension Model and Ride Comfort Improvement under Various Semi-Active Control Strategies

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results indicate that these control strategies can effectively reduce vibrations and impacts, significantly improving ride comfort. This improvement is crucial for alleviating driver fatigue and enhancing driving safety. Among them, the MPC control showed superior performance, reducing vibrations by at least 31% under both random and bump road conditions, outperforming PID and Fuzzy PID in terms of effectiveness and robustness.

Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions

Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation.

Numerical investigation and passive control of dust pollution on a minivan

A dynamic model using a discrete phase approach was developed to study dust pollution on a minivan's side and rear surfaces. Numerical simulations, validated by road tests, analyzed dust concentration and distribution. A significant factor in sidewall dust pollution is the pressure difference inside and outside the wheel cavity, particularly behind the wheel cavity, which affects about 40% of the sidewall. Large trailing vortices at the rear create a low-pressure zone, leading to rear dust pollution characterized by low concentration, large area, and dispersed regions. Aerodynamic devices were proposed to mitigate pollution. Simulations showed that side attachments, such as wheel deflectors, wheel cavity baffle, and mudguards, reduce aerodynamic drag and sidewall dust pollution, with wheel cavity baffles reducing the affected area by about 48.98%. Rear attachments, like diffusers and roof deflector, minimize rear dust pollution, with roof deflector reducing the area by approximately 87.24%. These findings highlight the effectiveness of aerodynamic modifications in controlling dust pollution on a minivan.

A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams.

Exploration of Traffic Accident-Based Pilot Zones for Autonomous Vehicle Safety Validation

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model’s classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method’s effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation.

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.