Fang Wang

Heuristic weaving structures (HWSs) have attracted wide attention in engineering fields due to their high energy absorption capacity, reliable load-bearing performance, and highly adjustable deformation characteristics. This study systematically investigates the mechanical properties of HWSs under different boundary constraints and sparsity degrees via finite element analysis (FEA). The results indicate that all models maintain stable force transmission and reliable structural integrity throughout the loading process. Boundary constraints and sparsity significantly affect the mechanical properties and energy absorption of HWSs. Under different boundary constraints, the tensile specific energy absorption (TSEA) increases consistently from 8616.84 J/kg to 15,212.20 J/kg, representing a 76.54% improvement. Under varying sparsity degrees, the TSEA rises continuously from 8616.84 J/kg to 15,468.65 J/kg (a 79.52% increase), while the tensile energy absorption (TEA) also exhibits an enhancement from 22.83 J to 67.44 J (a 195.40% increase). For the out-of-plane load-bearing (X-axis) performance of HWSs, weakening boundary constraints promotes stress redistribution and cooperative deformation, thereby enhancing energy absorption and load-bearing efficiency. In summary, the proposed HWSs exhibit substantial bearing and energy absorption performance improvements. This work enriches the design methodology of woven energy-absorbing structures and provides theoretical guidelines for the parametric design and potential future application of HWSs in lightweight and energy-absorbing engineering applications.

The intelligent vehicles are considered to be potential in avoiding traffic crashes caused by human errors. Reliable intelligent driving in China requires thorough understanding of complex drivers’ risk response difficulties in Chinese background traffic. Based on actual crashes between vehicles and powered two-wheelers (PTWs) in China, this study analyzed the drivers’ risk response difficulties to theoretically support the intelligent driving safety development. 113 crashes recorded in six typical vehicle-to-PTW risk scenarios were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. The psychological failures and behavioral errors of participants in each risk scenario were analyzed through descriptive statistics. The dominant factors contributory to drivers’ risk response difficulties and their interdependence were explored through the association rule mining (ARM). Furthermore, the heterogeneity of response difficulties, the characteristics of pre-crash spatio-temporal kinematics, and the influences for intelligent driving safety performance were also discussed based on actual crash data. The results showed that there was significant inter-/intra-heterogeneity in the difficulties encountered by drivers when dealing with various traffic risks. The evident differences of pre-crash spatio-temporal kinematics existed in different risk scenarios due to various evasive measures adopted by involved participants. A risk identification approach based on the assumption that the motion of each participant remained unchanged often failed to identify traffic risks early and even underestimated them. This study provided a theoretical basis for the construction of intelligent driving safety systems and their testing schemes tailored to the traffic environment in China.

In side-impact collisions, the occupant in the non-impacted far-side position faces a high risk of death and serious injury. However, current research on injury to far-side occupants remains limited. This study utilized 40 real-world side collision cases to extract dynamic boundary condition parameters of the impacted vehicle through kinematic reconstruction. These parameters were input into a simplified finite element (FE) vehicle model equipped with a human body FE model in the far-side position. Simulation calculations were performed to obtain head and chest injury parameters for the far-side occupant and assess their injury risk. Finally, the study focused on analyzing the effect of vehicle motion boundary conditions on far-side occupant’s injury risk. The assessment based on the head injury criterion HIC₁₅ shows a low head injury risk for the far-side occupant. However, using the BrIC metric, which accounts for head rotational motion, reveals a significant risk of severe traumatic brain injury in some cases. Regarding chest injury, analysis based on the effective plastic strain of ribs indicated a low risk of rib fractures. However, results from the chest viscosity criterion (VC) and internal organ strain analysis suggested a high risk of soft tissue injury in the chest. This computational investigation, leveraging biofidelic human models, underscores that the human body’s response to complex, multi-directional impacts is not fully captured by traditional metrics. This study concludes that addressing the protection of the far-side occupant is essential in side-impact safety design, with particular emphasis on the unique injury risks posed by vehicle rotational motion, potentially inspiring biomimetic safety systems that better adapt to these complex loading conditions.

With the iteration of vehicle brand design, the applicability of the current Mobile Progressive Deformable Barrier (MPDB) scheme has declined. The Advanced Chinese Mobile Progressive Deformable Barrier (AC-MPDB) is introduced, which incorporates segmented Z-directional design and increased mass distribution to better align with the Chinese vehicle front-end structure. This study utilizes Camry and Rogue models to design an MPDB-to-vehicle and a vehicle-to-vehicle simulation matrix. The compatibility of the barrier schemes, Euro-NCAP MPDB (E-MPDB) and AC-MPDB, is evaluated using energy equivalent speed, occupant load criteria, peak force, and acceleration. The simulation results indicate that, compared to E-MPDB, the AC-MPDB barrier scheme exhibits excessive stiffness for medium-sized sedans with smaller mass but demonstrates better compatibility with SUVs. Further analysis of the target vehicle's longitudinal beam deformation animation and pedal intrusion reveals that AC-MPDB achieves superior vehicle reproducibility, particularly for SUVs.

With the rapid growth in the number of electric vehicles equipped with advanced consumer electronics, the rate of impact accidents has also been rising year by year. Side pole impact tests are an important method for evaluating the collision safety of these modern electric vehicles. The purpose of this study is to gain industry insights into the kinematics and injury risk for far-side occupants in electric vehicle side pole impacts. This study uses a full-scale finite element model of an electric vehicle and a human body finite element model to conduct an in-depth analysis of the occupant’s kinematic response and the risk of injury to the head, neck, chest, and internal organs under various conditions by changing the relative position and impact angle between the rigid pole and the vehicle. The results show that the seatbelt fails to effectively restrict the upper body movement of the occupants, leading to the occupants slipping out of the seatbelt; the position of the impact significantly affects the injury risk to the occupants, with the highest probability of injury occurring during an A pillar impact and a lowest probability during a C pillar impact. In 28%–40% of the cases, the risk of far-side occupants sustaining serious head and brain abbreviated injury scale AIS 3+ injuries exceeds 40%, and in 22% of the cases, the probability of occupants sustaining diffuse axonal injuries based on which metric is higher than 40%; there is no correlation between the head injury criterion HIC15 and the impact angle, but a weak correlation exists between HIC15 and maximum principal strain (MPS); a strong positive correlation is found between the impact angle and brain injury criterion BrIC/MPS. The predicted MPS of nearly 40% and 80% of the far-side occupants’ anterior longitudinal ligament and posterior longitudinal ligament exceeds the injury threshold, respectively, while in all cases, the predicted MPS of the occupants’ capsular ligament and interspinous ligament exceeds the injury threshold, which indicates an extremely high risk of ligament injury. The peak strains of the internal organs of all far-side occupants exceed the threshold, indicating that the occurrence of these internal organ injuries mainly stems from a viscous mechanism, and the peak strains have a strong positive correlation with the impact angle.

As future intelligent vehicles are gradually brought into use, the vehicle driving scenario may show a predictable and highly diversified trend, which introduces a new challenge to the design of occupant injury protection in vehicle collisions. In this regard, quantitatively analyzing the sensitivity of occupant injury responses to highly diverse occupant postures and accurately predicting the risk of occupant injury related to specific OOP (out-of-position) postures are crucial for designing the crash safety of intelligent vehicles. In this study, a variety of typical occupant OOP postures are considered. Numerical simulations are conducted with existing occupant restraint systems and human body numerical models to investigate the correlation between OOP posture factors (such as AEB (automatic emergency braking) level, torso left/right tilt angle, body size, and seatback tilt angle) and the predicted injury responses. An OOP occupant injury prediction model for frontal collisions is established using BP (backpropagation) neural networks based on the simulation data. Our study shows that the OOP posture factors are correlated with various injury criteria to different extents. The seatback tilt angle is significantly and positively correlated with neck injury and is also clearly related to upper leg injury. The occupant torso left/right tilt angle is remarkably negatively correlated with chest injury, especially chest compression. The occupant size mainly affects lower leg injury, particularly tibia force, while the effect of AEB is less notable than these factors. The BP neural network-based model for the prediction of OOP occupant injury showed Adequate prediction performance in terms of consistency index (higher than 0.9 for all injury criteria) and prediction accuracy (higher than 84 for all injury criteria, except for the viscous criterion VC and neck extension bending moment). This study may serve as an important reference for the design of intelligent occupant restraint systems in crash events involving intelligent vehicles.

Examining 1192 intersection car and two-wheeled vehicle collision accidents from the China In-Depth Accident Study (CIDAS) database, this study employs population density heat maps for precise assessment of surrounding population densities at accident sites. The K-Medoid clustering algorithm and silhouette coefficient were used to classify accidents into two distinct groups based on population density. Subsequent application of the random parameter logit model revealed key contributing factors to these accidents in varying population densities. The results show notable differences in factors such as collision direction of two-wheeled vehicles, types of accident conflict, road conditions, and traffic flow, depending on the population density. Based on these conclusions, the research can inform differentiated risk prediction for two-wheeled vehicle accidents at intersections and provide insights for intersection design in various population density scenarios.