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.

In the context of vehicle intelligence, smart cockpits are widely used in modern vehicle design. However, with the popularity of smart cockpits, their impact on drivers’ driving behavior have not been evaluated. This study investigated the degree of visual-manual distraction and secondary task performance of drivers by four center screen layouts (single, joint, vertical and horizontal screens) in smart cockpits. Twenty-four drivers used the four smart cockpit layouts to complete three secondary tasks on real roads: a dashboard viewing task, a song switching task and a specified music playing task. Drivers’ visual-manual demands were measured by eye-tracking and task completion performance, and drivers’ cognitive demands were assessed by Likert scales. The results showed that the type of smart cockpit layout had a significant effect on both drivers visual-manual behavior and task performance, with the horizontal cockpit layout having the least effect on driver visual-manual distraction, while the vertical layout resulted in the highest level of visual demand and distraction. Performance on complex secondary tasks was more likely to be affected by the smart cockpit layout than on simple secondary tasks. Cognitive load was not significantly affected by the smart cockpit layout condition.

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.