To build a highly secure and trustworthy autonomous driving system in an open environment， this paper proposes a closed-loop learning method for autonomous driving decision-making algorithms in response to the long-tail distribution problem in autonomous driving scenarios. This method achieves algorithmic closed-loop through the generation of safety-critical scenarios and continuous learning. Firstly， for the basic algorithm that performs well in common driving scenarios， safety-critical scenarios with threat are generated to identify algorithmic flaws. Secondly， a continuous learning method that combines elastic weight consolidation and linear multi-strategy heads is adopted to further train the self-vehicle algorithm in safety-critical scenarios， avoiding the problem of catastrophic forgetting. Finally， the algorithm's adaptability to scenarios is enhanced through multiple closed-loop iteration. This paper takes the soft actor-critic algorithm as the basic algorithm to verify the effectiveness of the proposed closed-loop learning method. After two rounds of closed-loop iterative tests with significant environmental differences and continuously increasing difficulty， the collision rates of the two baseline methods without continuous learning strategy and only using experience replay strategy， and the method proposed in this paper are 25.40%， 25.33%， and 14.43% respectively. The comparison results show that the method proposed in this paper has a stronger comprehensive ability to resist catastrophic forgetting and explore new tasks. Therefore， the proposed closed-loop learning method can effectively improve the scene adaptability of learning-based autonomous driving decision-making algorithms and achieve iterative optimization of the algorithms.

For the technical challenges of Intelligent Vehicle Cyber-Physical System （IVCPS）， including fragmented and inconsistent multi-disciplinary models， the absence of a unified modeling methodology， and limited model reusability， which collectively hinder coordinated design and system evolution across the full lifecycle， this paper integrates object-oriented modeling concepts， Model-Based Systems Engineering principles， and component-based modeling to propose an IVCPS component-based modeling method featuring a closed-loop iterative process comprising multi-dimensional system decomposition， component modeling with semantic unification， intelligent component integration， multi-level collaborative verification， and model set construction and evolution mechanism. Firstly， the proposed method applies multi-dimensional decomposition to systematically partition IVCPS， clarify internal structural and interaction boundaries， so as to ensure modeling completeness and traceability. Secondly， component modeling with unified semantic description is employed to standardize the representation of multi-disciplinary models and resolve inconsistencies in modeling expressions. On this basis， an intelligent component integration mechanism is introduced to automatically determine component composition relationship and to construct system architectures in a standardized and structured manner， thereby improving the efficiency of heterogeneous component integration. Furthermore， a multi-level collaborative verification framework at both the component level and the system level is established to ensure the credibility and consistency of the modeling process and to guarantee model quality and reliability. Finally， based on verified standardized components， an extensible and reusable IVCPS model set is constructed， enabling hierarchical accumulation from atomic components to composite components and ultimately to system-level models， thus supporting continuous modeling and evolution of IVCPS across different application scenarios and lifecycle stages. A cooperative multi-bus operation system in the Taiheqiao Park is used as a case study for validation. The results show that the proposed method achieves a component reuse rate of 64%， demonstrating its effectiveness in addressing key modeling challenges in IVCPS， significantly improving modeling efficiency， and providing a systematic solution for intelligent transportation system modeling.

Driving safety has always been the primary concern in the field of autonomous driving. In recent years， intelligent vehicles have faced increasingly complex driving environment. To enhance the cognitive capabilities in such scenarios and improve the safety of driving strategies， a knowledge-data fusion-driven reinforcement learning algorithm is proposed in this paper. Firstly， the dynamic driving environment is abstracted into a complex network-based risk cognition domain model， effectively capturing the interactive relationship among vehicle nodes. Secondly， a safety-enhanced experience replay mechanism is introduced to fully exploit the information within the data. Finally， a reinforcement learning algorithm based on the safety-aware experience replay mechanism is proposed. Within the Actor-Critic framework， a safety evaluation module is incorporated， and driving recommendation derived from the risk cognition domain is integrated into the reinforcement learning training process. The experimental results show the proposed method achieves an 87% driving score and 81% success rate on the CARLA Leaderboard， improving autonomous driving safety.

Simulation-based autonomous driving function testing can effectively reproduce vehicle behaviors and states in real-world traffic scenarios， thereby enabling more accurate evaluation of autonomous systems' decision-making， planning， and control capabilities in complex environment. In this paper， a GAIL-GRU-based multimodal trajectory generation method for constructing autonomous driving simulation scenarios， integrating Triple-GAIL and GRU is proposed. Firstly， vehicle trajectories and multidimensional features are extracted from a human driving dataset. The trajectories are then categorized into three behavioral types—lane keeping， left lane change， and right lane change—with corresponding behavior labels. Next， vehicle states， behavior labels， and vehicle actions are formed into input triplets for the GAIL-GRU model. After training， a driving policy model with human-like behavior is obtained. Finally， autonomous vehicles controlled by an autonomous driving model and environment vehicles driven by the learned policy model are deployed in a static simulation environment to construct diverse traffic scenarios. In validation experiments involving lane-change cut-in scenarios by both environment vehicles and autonomous vehicles， the proposed method successfully reproduces a variety of human driving behaviors. The experimental results show that the generated scenarios by the proposed autonomous driving simulation scenario construction method effectively expose decision-making flaws of autonomous driving algorithms in complex interactive situations， with strong human-likeness and high-risk coverage. Moreover， the integration of the GRU unit into the adversarial imitation learning framework addresses issues such as mode collapse and loss of temporal information in multimodal trajectory generation. This enables coupled modeling of interaction intention and motion continuity in lane-change cut-in scenarios， overcoming limitation of traditional methods in modeling interactive game dynamics.

Path Tracking is one of the most important functions of autonomous vehicle. At present， autonomous vehicles’ lateral acceleration fluctuates during path tracking. In order to solve this problem this paper proposes an intermittent preview method for autonomous vehicle path tracking. Firstly， an event-triggered preview method is established. Whether the event is triggered is used as the judgment condition for determining whether to preview again. The event mechanism mainly considers path tracking deviation and maximum execution mileage. Then， a motion primitive based multi-segment preview method is established. The segmentation points are determined according to the preview tracking error. The distance of each segment is adjusted dynamically to ensure that the preview tracking error within the allowable deviation range. Finally， the effectiveness of the method proposed in this paper is verified and compared with various currently widely used methods in the simulation environment. The simulation test results show that the method proposed in this paper can ensure that the path tracking error within the acceptable deviation range and its planned target lateral acceleration is more stationary.

For balancing efficiency， safety and comfort during lane-changing of intelligent vehicles， this paper proposes a lane-changing trajectory planning method based on the iterative linear quadratic regulator （iLQR） algorithm. Firstly， in the lateral path planning， an improved quintic polynomial is used in the Frenet coordinate system to generate an initial lateral trajectory. In the longitudinal speed planning， a dynamic programming （DP） method with heuristic information is utilized to quickly generate a speed planning sequence that meets the kinematic constraints of the vehicle. Secondly， the initial trajectory is further optimized by the iLQR algorithm， incorporating collision risk， comfort and control constraints into the optimization objective， to obtain an efficient， safe and smooth optimal lane-changing trajectory. Finally， the method is verified through a joint simulation using CarSim， Matlab/Simulink and Prescan. The simulation results show that this method improves the lane-changing efficiency by approximately 20% compared to the traditional DP algorithm， with a more stable longitudinal acceleration， and significant improvement in the safety and comfort of lane-changing， which can provide efficient and reliable technical support for lane-changing decision-making of autonomous vehicles in complex traffic environment.

For unstructured road cleaning scenarios， existing multi-robot sweeper vehicles lack a collaborative optimization mechanism. They only achieve independent operation within sub-regions through area division and rely on simple loop operation to clear secondary pollution caused by dynamic obstacles， resulting in low overall system operation efficiency and severe resource waste. Therefore， this paper proposes a multi-robot sweeper vehicle collaborative control method based on a hierarchical architecture， to enhance the multi-vehicle collaborative operation capability in complex dynamic scenarios. At the planning level， a cleaning status information map and its update strategy are constructed to mathematically model and describe the dynamically changing cleaning status. Based on this， a self-organizing task allocation strategy is proposed to achieve dynamic and optimized task allocation among multiple vehicles. At the control level， a multi-vehicle collaborative controller based on a multi-agent anti-swarming control algorithm is designed， which can dynamically adjust the movement trajectories of vehicles using only local information， achieving collaborative operation and obstacle avoidance among multiple vehicles. The results show that compared with the method of using reciprocating full coverage path planning for each vehicle after area division， the proposed method can reduce the time required to complete full coverage of the task area by up to 58.72%， with the average and maximum time for cleaning secondary pollution grids reduced by up to 36.03% and 65.34% respectively. The stable average value of the coverage rate of cleaned grids can be increased by up to 10.13%， significantly enhancing the collaborative operation capability of unmanned sweeper vehicles in complex dynamic cleaning scenarios.

A Pareto-optimized topology switching control strategy is proposed for connected vehicle platoons to address two key challenges of dynamic changes in communication links that induce network topology switches and the scenario limitation of traditional manually designed topologies. The proposed approach aims to achieve a balanced trade-off among platoon stability， comfort， and energy efficiency. Firstly based on the Non-dominated sorting genetic algorithm， network connectivity is introduced as a constraint， while stability， comfort， and energy efficiency serve as optimization objectives to search offline for Pareto-optimized network topologies that effectively balance these performance metrics. Next， based on a distributed nonlinear model predictive controller， by integrating the Pareto-optimized topology with a Markov chain switching mechanism， a vehicle platoon nonlinear collaborative controller based on Pareto optimization topology switching is designed. Finally， simulation verification is conducted on the vehicle platoon controller， topology selection method， and switching mechanism. The experimental results show the effectiveness of the proposed method. The proposed approach outperforms traditional manually designed topology switching methods， improving tracking stability by 30.1%， comfort by 18.6%， and energy efficiency by 2.2%. These findings demonstrate the feasibility and effectiveness of the proposed cooperative control strategy in dynamic topology switching scenarios.

Predicting driver attention is of great significance for enhancing the safety of autonomous driving systems and achieving intelligent human-vehicle interaction. To this end， this paper proposes a driver attention prediction method based on the Conditional Denoising Diffusion Probabilistic Model （CDDPM）. In the forward diffusion process， a Markov decision process is introduced to gradually add Gaussian noise to the driver attention maps until they become random noise images. In the reverse denoising process， an inverse Markov decision process is performed， where a U-Net encoder-decoder neural network is employed as the noise prediction model to iteratively denoise the random noise images into predicted driver attention maps. Based on the denoising diffusion probabilistic model， spatiotemporal features are integrated into the reverse process through conditional guidance to ultimately predict the driver attention distribution. The experimental results show that the proposed method outperforms existing approaches across multiple metrics on the BDD-A and DADA-2000 datasets， validating its effectiveness and superiority.

To meet the increasing bandwidth demand of in-vehicle communication systems， this paper designs an electronic and electrical architecture based on passive optical networks and studies its bandwidth allocation strategy. By analyzing the delay and drawbacks of static bandwidth allocation and dynamic bandwidth allocation （DBA）， an improved DBA strategy is proposed. Firstly， the concept of service interval is introduced to reduce the waste of invalid resources by increasing the number of service interval cycles. Secondly， a flag bit is inserted into the XGEM frame header to reduce the DBA delay of the optical line terminal （OLT） and ensure high bandwidth utilization. Thirdly， the optimal cycle number and bandwidth allocation schemes under different scenarios are determined through iterative calculation. Finally， the simulation verification is completed on the NS-3 platform. The results show that the static bandwidth allocation strategy cannot flexibly configure resources and there is a waste of resources when the data volume changes. The traditional DBA strategy has low efficiency， which is difficult to meet the delay requirements under in-vehicle conditions. However， the improved DBA strategy can be adjusted in real time according to the actual transmission requirements， meet the delay requirements under different conditions， effectively improve the bandwidth utilization rate， and better meet the requirements of high bandwidth， low delay and high bandwidth utilization rate for future intelligent connected vehicles.

Road traffic accidents remain a critical public safety issue that demands urgent resolution due to the associated casualties and economic loss. When autonomous vehicles encounter unavoidable collision conditions， a key research focus in integrated active and passive safety is how to fully utilize the potential of vehicle active control and the adjustment capabilities of in-cabin restraint systems within extremely limited time windows to mitigate occupant injury risk. This study analyzes accident characteristics of both autonomous and human-driven vehicles to extract typical collision scenarios. Within these scenarios， vehicle trajectories are exhaustively examined to determine collision parameters across the research domain. Using a vehicle-to-vehicle collision simulation model and a high-biofidelity finite element model of a 50th percentile Chinese male， 64 finite element simulations are conducted， covering representative collision conditions. The results show the coupled effect of collision conditions and restraint system parameters on occupant injury risk. Across all collision scenarios， a reclined seating posture most significantly increases injury risk， elevating Head Injury Criterion （HIC） by 87% and Brain Injury Criterion （BrIC） by 59%. In medium-to-high speed collisions with normally seated occupants， optimizing collision conditions prove more effective in reducing injury risk than adjusting seatbelt pretension force. A pre-crash lateral adjustment of 1.0 m to optimize collision angle and position reduces the risk of MAIS3+ injuries by over 10% and chest compression by over 14%. This study explores the concept of an integrated active-passive driving risk field based on quantitative collision injury assessment， revealing differences in expected occupant injuries under integrated collision avoidance strategies and restraint system control. The findings provide a quantitative basis for optimizing pre-crash decision-making in autonomous vehicles and designing occupant protection systems.

Ramps are one of the key components in urban road traffic infrastructure. During the process of merging from a ramp onto the main road， the interaction between drivers are clearly highlighted. Most traditional studies on driving behavior interaction lack a comprehensive explanation of the mechanism from intention recognition to dynamic interaction and fail to provide adequate quantitative description methods. This paper addresses the challenge of quantifying drivers' dynamic interaction behavior in the on-ramp inbound scenario by leveraging the interaction dataset for data analysis and feature extraction. A driver's interaction field model with well-defined direction， shape， and intensity calculation is developed， transforming the qualitative description of driver interaction behavior into conflict quantification within the interaction field. Through computational experiments， it has been demonstrated that the established model can accurately identify and quantify the interaction behavior between drivers， thereby revealing the evolution mechanism from intention recognition to dynamic interaction through quantitative analysis.

With the development of autonomous driving technology， pedestrian crossing intention prediction has become an important approach to reducing pedestrian-vehicle conflict. However， traditional intention prediction methods fail to estimate the uncertainty of the prediction results， which leads to a lack of reliability in vehicle decision-making in complex traffic environment. To address this issue， this paper proposes an uncertainty pedestrian crossing prediction network （UN-PCPNet） with multimodal input. The network extracts features from the three types of input of pedestrian pose， bounding box， and vehicle speed， which are then sent to a feature fusion module for multimodal integration. The Bayesian multilayer perceptron module outputs the crossing intention prediction result， and the uncertainty of the prediction process is quantified through the variance analysis of the results. The proposed method achieves AUC scores of 92% and 89% on the JAAD and PIE public datasets， respectively， and provides reliable uncertainty estimation without compromising prediction accuracy. The real-world experiments also validate the effectiveness of this method in practical traffic scenarios， which can support the enhancement of autonomous driving safety.

The centroid sideslip angle is one of the key variables characterizing the stability of vehicle motion. Existing estimation methods mostly rely on high-precision vehicle dynamics and tire models， making it difficult to guarantee estimation accuracy under complex nonlinear operating conditions. To improve the accuracy and robustness of centroid side-slip angle estimation， this paper proposes a vehicle centroid side-slip angle estimation method that integrates the convolutional neural network-bidirectional long short-term memory network （CNN-BiLSTM） with the adaptive noise unscented Kalman filter （AUKF）. Firstly， a quantile estimation model based on CNN-BiLSTM is constructed， combining the vehicle kinematic model and the quantile regression loss function to estimate the mean and quantile intervals of the centroid sideslip angle. Secondly， a dynamic state observer based on AUKF is designed， using the estimation results of the CNN-BiLSTM observer to update the observation covariance matrix， achieving accurate estimation of the centroid sideslip angle. Finally， the proposed algorithm is verified through CarSim/Simulink co-simulation and real vehicle tests. The results show that the CNN-BiLSTM and AUKF integrated vehicle centroid side-slip angle estimation algorithm proposed in this paper has accurate estimation results under different operating conditions and road adhesion conditions， significantly outperforming the estimation methods based on dynamic models， with high estimation accuracy and strong robustness.

Road adhesion coefficient， as one of the indicators for assessing the amount of friction between road surface and tires， influences the decision control strategies for autonomous driving systems. For the problem of road adhesion coefficient estimation， this paper proposes a road adhesion coefficient estimation method based on physical information neural network （PINN）. Firstly， the vehicle dynamics analysis is carried out to construct a seven-degree-of-freedom vehicle dynamics model. Based on the Dugoff tire model， the accuracy of the nonlinear region of the model is improved to obtain an improved two-degree-of-freedom dugoff tire model. Then， the relationship between the kinetic parameters and the road adhesion coefficient is analyzed to determine the input to the neural network， and the network model for road adhesion coefficient estimation is constructed by combining the attention mechanism and the long and short-term time series model. Finally， based on vehicle model and tire model， the vehicle dynamics equations are introduced to construct the physical constraint loss function， and the PINN network is constructed. The experiments show that the introduction of the loss function with physical model increases the convergence speed of the model compared to the model without physical constraints， and reduces the mean absolute error by 44.68％ and the root mean square error by 39.87％.

High-precision vehicle roll dynamics modeling is crucial for enhancing the performance of active safety control systems. However， the vehicle system is characterized by strong nonlinearity and parameter uncertainty， making it difficult for traditional mechanism-based modeling methods to accurately estimate roll angle and roll angle velocity. To address this issue， this paper proposes a data modeling method that integrates multi-scale convolutional neural networks with Transformer. This model utilizes multi-scale convolutional kernels to extract multi-frequency domain features from noisy data， enhancing the model's robustness against noise interference. Meanwhile， by combining the attention mechanism of Transformer， it effectively captures the long-term temporal dependencies in roll dynamics， further improving the modeling accuracy. To verify the model's performance， this study conducts a comparative analysis with traditional data-driven models such as Transformer， LSTM， GRU， and physical models based on high-fidelity CarSim simulation platform and real vehicle road test data. The experimental results show that the proposed CNN-Transformer hybrid model performs optimally in the tasks of roll angle and roll angle velocity prediction， with prediction determination coefficients R² all above 0.974 5， achieving accurate modeling of vehicle roll dynamics.

Aiming at the motor faults and unknown disturbances of four-wheel independent drive electric vehicles， this paper proposes a hierarchical fault-tolerant control strategy to achieve the controllability of tracking errors under sudden fault conditions. Firstly， the motor fault characteristics of four-wheel independent drive electric vehicles are analyzed， and a dynamic model including motor fault modes and unknown disturbances is constructed. Secondly， a neural network observer is designed to estimate the fault signals and unknown disturbances in real time. Then， an upper-level controller is constructed by integrating the preset performance control， Nussbaum-type function and barrier Lyapunov function， and the finite-time convergence characteristics of the closed-loop system are strictly proved by Lyapunov stability theory. Next， a torque optimization model is established based on the motor output constraints under fault conditions to achieve the coordinated control of the front wheel steering torque and the four-wheel driving torque. Finally， the CarSim/Simulink co-simulation platform is used to conduct comparative verification in three typical scenarios： single-wheel failure， steering failure and double-wheel failure. The results show that in the case of double-wheel failure， the longitudinal， lateral and yaw angle tracking errors are reduced by 74.6%， 79.6% and 89.7%， respectively.

Effective health indicators （HI） and predictive methods are crucial for assessing the remaining useful life （RUL） of proton exchange membrane fuel cell （PEMFC）. However， directly obtaining HIs under complex dynamic load conditions poses significant challenges. Traditional deep learning methods， constrained by their one-dimensional data analysis framework， struggle to accurately predict the aging trends of PEMFCs， which encompass various modes of degradation. To address this， our study proposes a novel prediction framework tailored for dynamic operating conditions. This framework involves a hierarchical processing of dynamic loads to generate pseudo-dynamic data that encapsulates aging trends. Subsequently， a method that combines empirical mode decomposition （EMD）， power spectral density （PSD）， and energy analysis （EA） is proposed to extract HIs that reflect aging trends under dynamic conditions. Furthermore， in terms of prediction methods， the TimesNet network is utilized to transform one-dimensional HI time series into two-dimensional space and estimate RUL. The results demonstrate that the extracted HIs effectively capture the aging trends of PEMFCs. In terms of RUL estimation， TimesNet reduces the prediction error by 61.8% compared to bidirectional long-short term memory （BiLSTM） and by 25% compared to bidirectional long short-term memory with convolutional neural network （CNN-BiLSTM）.

In recent years， the continuous improvement of thermal efficiency development targets for hybrid dedicated engines has made high compression ratio application increasingly common. However， the increase in compression ratio is accompanied by problems such as an increase in abnormal combustion tendency. The direct water injection technology， which directly sprays liquid water into the cylinder， fully utilizes the latent heat of evaporation during water mist injection and its interactive effects on the combustion process， which can achieve reduced in-cylinder temperatures， increased charge density， and optimized combustion phasing， thereby improving the thermal efficiency of hybrid dedicated gasoline engines， having gradually gained widespread attention in the industry. In this paper， taking a hybrid dedicated gasoline engine as the research object， by developing a high-precision combustion system model and maintaining constant pressure rise rate constraints， simulation research is conducted to investigate the effect of direct water injection design schemes and control strategies on combustion processes and emission characteristics. The results show that the optimal water injection strategy involves head mid-position water injection at -135°CA timing with a 12 mg cycle water injection quantity （40% water-oil ratio）. Combined with optimized ignition strategies， the indicated thermal efficiency can be increased from the baseline engine's 35.36% to 42.06%. Concurrently， CO emission decreases by 1.23 mg compared to the baseline engine， while NO_x increases by 0.315 mg and UHC increases by 0.006 mg.

At present， opening and closing of exhaust brake mainly rely on the driver， which requires high driving skills， and there is the problem of brake thermal degradation caused by improper operation， so this paper proposes to develop the active control system of exhaust brake based on the recognition of operating conditions. Firstly， based on the statistical analysis of brake pedal action data from typical mountainous roads， the K-means++ clustering algorithm is applied to classify driving conditions using three characteristic parameters of the average brake pedal opening， the average brake pedal action ratio and braking times， under different time window lengths， and the RNN algorithm is employed to achieve driving condition recognition. Then the exhaust brake control mechanism based on the dynamic priority arbitration is proposed， and the active control system is developed based on the models of vehicle longitudinal dynamics， braking system， engine and exhaust brake， brake drum temperature， etc. The effectiveness of the exhaust brake active control system is verified online by building a semi-physical simulation platform， and the validation results show that the active control system can accurately identify the driving conditions and autonomously activate or deactivate the exhaust brake in a timely manner， which is the same as that of the offline environment and the error of the control time is only 3.294%.

With the continuous popularization and rapid development of new energy vehicles， the safety issues have received increasing attention. When new energy vehicles encounter collision， the chassis and battery packs are highly susceptible to damage， which may lead to electrolyte leakage and subsequently cause short circuits， fires， and explosions. Therefore， this paper draws inspiration from the stable structure of DNA to improve the traditional honeycomb structure and designs a novel DNA double helix-shaped protective structure for the bottom of power battery boxes. A simulation analysis of foreign object impact on the bottom of power battery boxes is conducted. Compared with the traditional honeycomb structure， the specific energy absorption of this new protective structure increases by 12.14%. On this basis， this paper combines machine learning and multi-objective optimization algorithms to optimize the DNA double helix-shaped protective structure and obtain its optimal design parameters. When a foreign object penetrates vertically upward at a speed of 30 m/s， compared with the initial protective structure， the energy absorption of the optimized protective structure increases by 44.3%， with the peak crushing force decreasing by 35.68%， and the maximum compression of the battery decreasing by 68.1%. This research result can provide scientific theoretical support for the design of protective structure for new energy vehicle power battery boxes and is of great significance for ensuring the safety of new energy vehicles in collision conditions.