Rapid measurement of steel surface defects is of utmost importance in modern industrial manufacturing. However, challenges arise due to the complex textures and varied morphological patterns of defects. This paper presents YOLO-APEX, an adaptive, lightweight structure for steel surface defect detection. Key innovations include the Multi-Scale Feature Extraction Foundation (MSFEF) module, the Context-Aware Enhancement (CAE) module, and the Adaptive Group Attention Optimization(AGAO) module. The MSFEF captures deep features through horizontal and vertical decomposition at multiple scales, the CAE enhances the understanding of contextual relationships between defects and surrounding textures, and the AGAO integrates a dynamic segmented attention optimization mechanism and a dynamic cardinality grouping strategy to achieve fine-grained, pixel-level feature weighting. Experimental results prove that YOLO-APEX attains a 33.5% mAP 75 on the Steel Surface Defect Detection dataset, outperforming the baseline (29.2% mAP 75 ),and a 43.1% mAP 75 on the NEU-DET dataset,outperforming the baseline (41.8% mAP 75 )while maintaining fewer parameters.

Deep reinforcement learning (DRL) has demonstrated strong potential for energy management systems. However, existing approaches struggle with the complex nonlinear dynamics of hybrid energy storage systems (HESS), especially due to gradient attenuation under extreme conditions, which undermines control stability. This paper proposes a Residual-enhanced Proximal Policy Optimization (ResPPO) algorithm, which integrates residual connections into the policy network. This design enhances gradient flow propagation and mitigates vanishing gradients, leading to better training stability and optimization performance. Simulation experiment results under UDDS conditions show that ResPPO achieves a 31.7% reduction in battery degradation costs compared to standard PPO while delivering superior supercapacitor state-of-charge (SOC) regulation.

Three-dimensional lane detection is a fundamental yet highly challenging task in autonomous driving, as the presence of interference and blurring in images often impedes accurate detection. To address these challenges, we propose the MRDALane framework, which introduces two novel modules: the Dual Channel Attention Module (DCAM) and the Multi-Resolution Context Augmentation (MRCA). The DCAM utilizes a dual-channel attention mechanism to effectively suppress noise and emphasize salient features, significantly enhancing lane detail capture in complex environments. The MRCA incorporates multiple dilated convolutions with a sawtooth dilation rate design, enabling diverse feature learning across branches and improving robustness across different road conditions. Experimental results demonstrate that MRDALane outperforms state-of-the-art (SOTA) 3D lane detection models, such as LATR and CurveFormer, on both the Apollo and OpenLane datasets. Notably, in robustness experiments under adverse conditions, MRDALane achieved an F1 score improvement of up to 13.66% compared to the LATR model. This comprehensive performance evaluation demonstrates our model’s superior detection accuracy across various challenging scenarios. These advancements provide new insights for future research in 3D lane detection, contributing to the ongoing development of autonomous driving technology and road safety. Our code will be released at https://github.com/Dcelysia/MRDALane.git.

To mitigate the impact of model uncertainties and the nonlinear dynamic characteristics of actuators on the control of heterogeneous vehicle platoon, this study proposes a distributed data-enabled predictive control (DeePC) strategy. This strategy can effectively guide unknown dynamic systems to move along expected trajectories while satisfying system constraints. Firstly, a non-parametric model of the vehicle system, which takes into account the nonlinear dynamic characteristics of the actuator, is established using input-output (I/O) vehicle trajectories. Then, the regularized DeePC and the distributed control are integrated and applied to the data-driven control of vehicle platoon based on the non-parametric model. Furthermore, a local cost function, considering other vehicles information and constraints, is designed to optimize the control problem of vehicle platoon. Finally, the simulation results verify the effectiveness of the distributed data-enabled predictive control strategy in vehicle platoon control. Compared with distributed nonlinear model predictive control, the proposed distributed data-enabled predictive control exhibits stronger robustness.

As autonomous driving technology continues to mature, ensuring that vehicles can navigate safely and stably in complex scenarios has become a critical issue that needs to be addressed urgently. Ramp merging, as a common and important segment of the road, is crucial for improving overall road safety by ensuring that autonomous vehicles can operate safely and efficiently in this area. Therefore, a decision-making model for autonomous vehicles based on deep reinforcement learning (DRL) is constructed to address the ramp merging problem on highways. First, a highway ramp merging decision-making model is designed based on the Dueling Deep Q-Network (Dueling-DQN) algorithm. Then, the traffic simulation software SUMO and the Python programming language are used to build the simulation training and validation environment for the model to evaluate the effectiveness of the proposed autonomous vehicle decision-making model. Simulation results show that Dueling-DQN significantly improves the success rate of autonomous vehicles during the ramp merging process. Compared to the DQN algorithm, the Dueling-DQN algorithm not only achieves higher average returns but also converges faster, completing the ramp merging task more quickly. This validates the superiority of the Dueling-DQN algorithm in the highway ramp merging problem and demonstrates its high application value in autonomous vehicle decision-making.

Pose prediction and trajectory forecasting represent pivotal tasks in the realm of autonomous driving, crucially enhancing the planning and decision-making capabilities of self-driving vehicles. However, a prevailing challenge is that many existing algorithms for these tasks necessitate supervised training, demanding substantial human effort and computational resources. To alleviate this resource-intensive burden, this paper introduces an innovative method for predicting future object poses and trajectories in a 3D space, obviating the requirement for manual annotations. The enhanced self-supervised 3D point cloud prediction algorithm proposed in this study incorporates an 3D Action Attention module, augmenting TCNet’s proficiency in extracting vital spatiotemporal and motion information from continuous point cloud range images. Additionally, 3D Octave Convolution is harnessed to mitigate the computational overhead introduced by the 3D Action Attention module, consequently accelerating the model’s inference speed. This advanced self-supervised 3D point cloud prediction algorithm is denoted as TSMNet. TSMNet outperforms the baseline TCNet and several SOTA 3D point cloud prediction models when evaluated on the KITTI Odometry dataset. Moreover, it showcases robust generalization capabilities in unfamiliar environments. Notably, TSMNet can predict future point cloud data for five frames in a mere 33 milliseconds, surpassing the frame rate of typical LiDAR sensors, which typically operate at 10Hz. Furthermore, when integrated with a point cloud clustering and tracking algorithm, the improved self-supervised 3D point cloud prediction algorithm facilitates the extraction of object poses and trajectories. The performance metrics of the point cloud clustering and tracking algorithm attain remarkable levels of accuracy, with a Multiple Object Tracking Accuracy (MOTA) of 86.12% and a Multiple Object Tracking Precision (MOTP) of 91.01% on the KITTI dataset.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

Intelligent connected vehicle platoon technology can reduce traffic congestion and vehicle fuel. However, attacks on the data transmitted by the platoon are one of the primary challenges encountered by the platoon during its travels. The false data injection (FDI) attack can lead to road congestion and even vehicle collisions, which can impact the platoon. However, the complexity of the cellular - vehicle to everything (C-V2X) environment, the single source of the message and the poor data processing capability of the on board unit (OBU) make the traditional detection methods’ success rate and response time poor. This study proposes a platoon state information fusion method using the communication characteristics of the platoon in C-V2X and proposes a novel platoon intrusion detection model based on this fusion method combined with sequential importance sampling (SIS). The SIS is a measured strategy of Monte Carlo integration sampling. Specifically, the method takes the status information of the platoon members as the predicted value input. It uses the leader vehicle status information as the posterior probability of the observed value to the current moment of the platoon members. The posterior probabilities of the platoon members and the weights of the platoon members at the last moment are used as input to update the weights of the platoon members at the current moment and obtain the desired platoon status information at the present moment. Moreover, it compares the status information of the platoon members with the desired status information to detect attacks on the platoon. Finally, the effectiveness of the method is demonstrated by simulation.

With the connected vehicle (CV) can interact with the infrastructure and can be used as a moving sensor to bring more real-time and higher precision input for intersection signal control. However, such connectivity also brings network security risks.To protect the signal security of intersections, this paper designs a realistic signal attack model based on forged trajectory injection to simulate the potential attack that a smart attacker may launch, the key track points of queued vehicles were extracted, the traffic wave velocity of queued vehicles was used as the distance index to transform forged track recognition into an outlier detection problem, and a forged track detection algorithm based on hierarchical clustering was proposed. Experimental results show that under different attack targets and permeability, the highest detection rate is 95%, and the lowest is 67%. This method does not require training, learning and high computation power of edge equipment. Therefore, it has a certain potential for intersection signal timing using CV trajectory.

The safety of railway crossings are of great important for rail and road transportation, because serious accidents occur in this area. Therefore, it is necessary to carry out foreign objects detection on railway crossings in order to improve the safety. Traditionally, video surveillance is one such solution, but it suffer from weather and illumination conditions. Under the hard environment conditions, the image of railway crossings is failed to capture by the camera. We propose a foreign objects detection system based on millimeter wave radar which has a higher detection accuracy, without the limitation of weather and light. Unlike vision-based approach, it can operate in darkness, high or low light intensity environment. With a millimeter wave radar, we first obtain the reflected signal from objects or ground and perform signal processing algorithm to extract the targets and suppress the clutter from received signal. We evaluate the detection capabilities of the millimeter wave radar in level crossings of railway.

Logistics distribution is an important link in logistics. Whether the logistics distribution path can be effectively optimized will directly affect the efficiency of the logistics distribution system. To plan the logistics distribution path reasonably, to reduce the cost of logistics management, for the multi-object path planning problem in logistics distribution, the fireworks differential evolution algorithm is used to design an optimization scheme. To achieve the overall goal of saving logistics and distribution costs, real number coding is used for each distribution point, and actual road information is obtained through the Gaode API. Aiming at the defects of the standard fireworks algorithm, the differential evolution algorithm is introduced based on the fireworks algorithm to plan the distribution route. The simulation results show that the firework differential evolution algorithm can effectively plan the optimal distribution path, and compared with the original firework algorithm, the ant colony algorithm and particle swarm optimization algorithm have a better improvement in the optimization accuracy.

The Electronically Controlled Pneumatic (ECP) brake system is being applied to the heavy-haul trains for its control accracy and real time performance. A high power supply with high efficiency and reliability is essential to the safe operation of the ECP brake system. In this paper, a push-pull forward converter with a secondary full bridge rectifier is proposed for the ECP power supply. The voltage stress of the rectifier diodes can be reduced by a modified nodissipative snubber. Furthermore, hybrid control methods and criteria are utilized to improve the converter performance, including forced flux balancing circuit design, power supply output impedance and noise requirements. Finally, A 2.5kW prototype of the converter with efficiency up to 93% verifies the proposed circuit and theoretical analysis.