Near-field communication in vehicle ad hoc networks can effectively reduce communication overhead compared to peer-to-peer communication. However, there is still plenty of room for improvements to be made to ensure identity authentication privacy protection and to enhance the security and efficiency of key distributions during transmissions. Therefore, this article proposes an anonymous identity authentication and group key distribution scheme based on quantum random numbers. In the proposed scheme: 1) anonymous credentials for vehicles are generated by a combination of random numbers on the vehicle side and random numbers in the trusted authority (TA), and mutual recognition of vehicles and roadside identity is achieved through the TA in the form of zero-knowledge proof, which achieves privacy protection for the vehicle during authentication and 2) a combined key generation method was devised. The roadside and the TA in this case jointly generate the group key. The TA uses a previously filled quantum key to encrypt the group session key parameter GSP(c) generated by its quantum random number generator to ensure security, and the roadside obtains the group session key parameter GSP(r) by calculating the anonymous credentials of all legitimate vehicles to achieve fast updates of the group session key. This scheme achieves forward and backward security while guaranteeing one-at-a-time encryption. The signaling and computation overheads were calculated, and the signaling overhead was reduced by nearly half. In addition, the group key issuance time was significantly reduced compared with other schemes. Through formal security analysis and experimental verification, the security and feasibility of this protocol were proved.
In addressing the trade-off between prediction model accuracy and computational cost in the context of path tracking control, this paper proposes a learning-based model predictive control (LB-MPC) strategy for autonomous buses. A three-degree-of-freedom (DOF) single-track vehicle dynamic model is established, and an in-depth analysis is conducted on its step response error with respect to variations in vehicle speed, pedal position, and front wheel steering angle compared to the IPG TruckMaker model. Methods for constructing error datasets and receding horizon updates are designed, and a Gaussian process regression (GPR) is employed to establish an error fitting model for real-time error compensation and correction of the nominal single-track model. The error correction model is utilized as the prediction model, and a path tracking cost function is designed to formulate a quadratic programming (QP) optimization problem, proposing an LB-MPC path tracking control architecture. Through joint simulations using the IPG TruckMaker & Simulink platform and real bus experiment, the real-time performance and effectiveness of the proposed GPR error correction model and LB-MPC path tracking control strategy are verified. Results demonstrate that compared to traditional MPC path tracking control strategy, the proposed LB-MPC strategy reduces the average path tracking error by 79.00%.
For the problem of high temperature and uneven distribution affecting the power and safety of the electric vehicle during the battery management systems slave control board's service,a commercial BMS slave control board thermal analysis model is built and verified using the CFD theory and Icepak software.For the first time under vehicle service conditions,temperature field analysis and thermal uniformity optimization research are carried out based on the thermal analysis model.The BMS slave control board thermal simulation analysis shows that the balancing and power supply modules exceed the BMS's design temperature limit of 60 ℃ due to local heat accu-mulation,with the maximum temperature difference of the entire BMS slave control board being 21.0 ℃.A heat dis-sipation path analysis of the BMS slave control board is further carried out,and heat dissipation optimization design is realized by altering the distance,layout of the balancing resistor,PCB substrate and adding thermal pads.By in-creasing the heat dissipation capacity of the BMS slave control board,the highest temperature of the BMS slave con-trol board can be controlled below the design limit of 60 ℃,and the temperature difference of the entire circuit board can be reduced to 6.9 ℃,which enhances the safety and reliability of the BMS slave control board under actu-al vehicle service conditions,providing theoretical methods for the thermal design and optimization of the BMS slave control board.
During the pure electric vehicle high speed cruise driving condition, the unsteady air flow in the chassis cavity is susceptible to self-sustaining oscillations phenomenon. And the aerodynamic oscillation excitation could be coupled with the cabin interior acoustic mode through the body pressure relief vent, the low frequency booming noise may occur and seriously reduces the driving comfort. This paper systematically introduces the characteristics identification and the troubleshooting process of the low frequency aerodynamic noise case. Firstly, combined with the characteristics of the subjective jury evaluation and objective measurement, the acoustic wind tunnel test restores the cabin booming phenomenon. The specific test procedure is proposed to separate the noise excitation source. Secondly, according to the road test results, it is inferenced that the formation mechanism of low frequency noise is the self- sustaining oscillation with the underbody shedding vortex feedback enhancement mechanism at the bottom of the rear chassis. The low frequency exterior airflow oscillation is coupled with the interior cabin acoustic modality by the two air relief vents located at the vehicle rear body parts. Furtherly, the modal coupling mechanism is verified by the volumetric acoustic source excitation test in the semi-anechoic chamber. Considering the engineering feasibility and cost, some improvement schemes are proposed and verified by comparison. Finally, the cover of the vehicle body pressure relief vent is determined to the actual application, the cabin noise level in the specific low frequency band is reduced by 10 dB(A). This paper provides a guiding reference for solving the similar low frequency noise problem of electric vehicles at the high-speed cruise condition.
At present,the unsupervised monocular infrared image depth estimation method is difficult to deal with low texture and low contrast areas,resulting in poor estimation effect,so an unsupervised monocular infra-red image depth estimation algorithm based on local plane guide layer is proposed in this paper.The algorithm con-sists of continuous video frame input,multi-scale feature extraction,ASPP and local planar guidance layer,compu-tational loss,joint training,and output image module.Firstly,by using multiple small-resolution grayscale blocks and multi-scale feature fusion,the problems of blurring edges and occluding objects in infrared images are solved.Secondly,by using the local plane guidance layer to introduce a plane constraint on the depth image,the noise and discontinuity in the depth image are reduced,and the problem of lack of clear processing of low texture areas of the traditional algorithm is solved.The experimental results show that the proposed depth estimation algorithm effective-ly improves the accuracy of monocular depth estimation and reduces the error,and the Abs Rel,Sq Rel,RMS,RMS(log)on the Iray dataset is 0.262,3.621,9.473 and 0.332,respectively,and the accuracy reaches 60.5％,85.2％ and 94.5％ when the threshold indicators are less than 1.25,1.252 and 1.253.
