This paper aims to improve the existing hybrid A* algorithm in view of the issues such as excessive reliance on resolution and paths being overly close to obstacles. Firstly, the distance calculated by KD-Tree is employed as a penalty term and incorporated into the cost function of the hybrid A* algorithm. Secondly, the node expansion distance is dynamically modified in accordance with the distance between the vehicle and the obstacle to realize the dynamic expansion of nodes, ensuring search efficiency while enhancing safety. Then, each node undergoes curvature-based cyclic checking to recall nodes that meet the Reeds-Shepp (RS) curve generation conditions but are previously overlooked due to fixed RS curve curvature. Finally, the curvature of the RS curve is dynamically altered based on the distance between the vehicle and obstacles, enabling the generated RS curve to have a moderate curvature and maintain a safe distance from the obstacles. Experiments demonstrate that, compared with traditional algorithms, the proposed algorithm shortens the search time by 7.18%, reduces the maximum curvature by 63.63%, and increases the minimum distance between the path and the obstacle by 143.94%, effectively enhancing the quality of the generated path.

To address the problem that existing trajectory prediction models lack explicit modeling of driving intention and thus struggle to accurately predict diverse driving behaviors, this paper proposes a Goal-oriented Interactive Trajectory Forecasting with Dynamic Attention (GITF-DA). Employing a multi-head attention mechanism to extract temporal and spatial features of vehicles, a lightweight shifted attention module is introduced to capture key behavioural characteristics during social interactions. Combining Bidirectional Long Short-Term Memory (Bi-LSTM) network with Shifted Window Attention (SWA) for driving intent recognition, generating directional targets within a Conditional Variational Auto-Encoder (CVAE) module. Leveraging the diversity of generated targets to achieve multimodal trajectory prediction. The results indicate that in lateral and longitudinal intent recognition tasks, the proposed model achieved accuracies of 98.01% and 84.27% respectively, with Macro F1 scores of 81.81% and 81.61%. In trajectory prediction tasks, the model demonstrates low prediction error.

To address the increased fault probability caused by the growing number of actuators in all-wheel-steering vehicles, this study focuses on motion control of a 3×3 all-wheel-steering intelligent vehicle and proposes an infinite-horizon Model Predictive Control (MPC) method that accounts for actuator input delay and faults. The algorithm decomposes the delay-aware optimization into an MPC solved over stages 0~N-1 and an LQR solved over N~ ∞ , and introduces Cholesky decomposition in the MPC stage to compute control inputs, avoiding factorization of large Karush-Kuhn-Tucker (KKT) matrices. Simulation and experimental results show that the proposed control strategy effectively achieves fault-tolerant control when actuator faults occur, while offering higher stability and control accuracy compared to traditional LQR and MPC methods.

To achieve accurate emission simulation for car-following vehicles, this paper proposes a calibration and optimization method based on minimizing the error between the Vehicle Specific Power (VSP) distribution of car-following models and the measured lead vehicle. By constructing an objective function to minimize VSP distribution error, the parameters of the car-following models are calibrated to enhance the model's realism. Using measured emission data of the lead vehicle, the emission characteristics of various car-following models are fitted and compared. The results show that Gipps, IDM, OVM, GFM, and FVDM models all demonstrate favourable VSP distributions and effective fitting of following vehicle emission; The Gipps model demonstrated optimal performance in VSP distribution fitting accuracy (mean Root Mean Square Error (RMSE) of 0.003 7) and emission factor fitting for CO₂, NO_x, CO, and Partical Number (PN) emission factors (mean relative error to the reference vehicle of 0.93%). In multi-vehicle platooning emission simulations, the Gipps model exhibits the smallest emission fluctuations. The mean relative errors for CO₂, NO_x, CO, and PN emission factors are 0.48%, 0.52%, 0.87%, and 1.89% respectively. The model demonstrates optimal consistency in fitting emissions across all platooning vehicles.

To enhance the security and immediacy of wireless channel cloud service access for intelligent connected vehicles, a blockchain-based anonymous identity authentication scheme is proposed. This paper employs the Bennett-Brassard 1984 (BB84) protocol to establish key agreements among multiple cloud servers, thereby enabling vehicle connectivity with these cloud resources. Anonymity certificates generated via quantum random number generation at the vehicle end ensure privacy protection during authentication. Blockchain technology is utilized to record member vehicle information, thereby reducing authentication communication rounds and enhancing operational efficiency. The results indicate that the proposed solution entails a computational overhead of 711.7 μs at the vehicle end and a communication overhead of 412 B, combining both security and efficiency.

This paper proposes a wind-solar driven hybrid blue-green hydrogen system integrated with heterogeneous traffic flows. This approach addresses the limitations of single hydrogen production and the absence of scheduling mechanisms. To handle resource stochasticity, the study employs Monte Carlo simulations and a cluster aggregation algorithm. These methods transform vehicles into dispatchable virtual units. A mixed-integer linear programming (MILP) model is then established to minimize the total operating cost. The results demonstrate that, compared to the benchmark scenario without traffic integration, the proposed synergistic optimization reduces the Levelized Cost of Energy of the system by 14.5%. The fluctuation rates of green and blue hydrogen are 5.29% and 7.24%, respectively. This study provides a feasible solution for the coordinated scheduling of high-penetration renewable energy and transportation-energy fusion systems.

In order to verify the applicability of dummies in the AEB braking superimposed rear-end collision, this paper takes BioRID-II dummies as the object, and analyzes the impact of different factors on the dislocation of the dummy head by setting different vehicle traction braking schemes. Furthermore, the spring mass model is used to perform equivalent analysis of the off-position of the dummy head to provide theoretical support for the off-position of the dummy head, and correct the off-position data of the dummy head to perform bionic analysis of the dummy head. The results show that the head dislocation amount of BioRID-II dummies has a high consistency with the tense volunteer data. The bionic degree of the BioRID-II dummy meets the requirements of the AEB braking superposition rear-end collision and can be used for the pre-braking superposition whiplash test.

To address the challenges faced by domestic automotive chip manufacturers in developing the Microcontroller Abstraction Layer (MCAL) for the Automotive Open System Architecture (AUTOSAR)-such as insufficient technical expertise and prolonged development cycles, this paper proposes an innovative layered architecture design. This approach redefines the MCAL into 3 distinct layers. Through functional decoupling and standardized interfaces, it achieves compatibility with both AUTOSAR and traditional Software Development Kit (SDK) development models. Validation on Xiaohua Semiconductor's XC277 chip demonstrates that all modules passed unit and integration tests with 100% coverage (including statement, branch, and Modification Condition/Decision Condition Coverage (MC/DC), and critical task execution times reached microsecond levels. This research provides a well-validated solution for technical innovation in domestic AUTOSAR MCAL, holding significant practical implications for enhancing the autonomous and controllable capabilities of China's automotive electronic software.

Based on the SMIC 12 nm Fin Field-Effect Transistor (FinFET) process, this paper analyzes the harmonic distortion and bandwidth performance indicators of the track/hold circuit at the front end of the Analog-to-Digital (A/D) converter. By reasonably selecting the type and size of the track/hold switch, the harmonic distortion caused by the temperature variation of the track/hold switch is reduced, and a new type of switch output buffer is proposed to improve the linearity of the track/hold circuit and the driving capability of the A/D converter. Test results show that: using the gate and parasitic capacitances of N-type Metal-Oxide-Semiconductor (NMOS) and P-type Metal-Oxide-Semiconductor (PMOS) transistors as sampling capacitances, the overall front-end circuit bandwidth is increased to 34 GHz through simulation verification with Cadence Virtuoso; when the sampling frequency f_s=10 GHz and the input signal f_in=9.8 GHz, the Spurious-Free Dynamic Range (SFDR) and Total Harmonic Distortion (THD) reach 67 dB and -59.5 dB, respectively. The proposed design scheme can be applied to the built-in self-test and self-check safety system of autonomous driving, reducing the failure rate of the autonomous driving system and improving the reliability of the embedded vehicle chip safety mechanism.

Regarding failure cases of Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs) during thermal shock testing, failure defects and causes are analyzed through electrical testing, ultrasonic scanning microscopy, super-depth three-dimensional microscopy, scanning electron microscopy, and other methods. Corresponding solutions are provided, which hold value for enhancing the reliability of SiC MOSFETs and ensuring stable operation of the full vehicle.