We address the challenge of integrating high-level semantic reasoning with low-level trajectory planning in end-to-end autonomous driving, where most existing frameworks decouple perception, decision-making, and control, leading to limited interpretability and poor instruction compliance. To bridge this gap, we propose Driving with Advice, a novel closed-loop framework that treats a vision-language model (VLM) as a motion advisor to provide interpretable, language-mediated guidance for trajectory generation. Our approach introduces three key innovations: (1) Semantic-Intentional Pretraining (SIP), which injects driving rationale into a compact VLM via machine-generated question-answering pairs; (2) a discrete action space grounded in directional and speed primitives, enabling structured and interpretable policy learning; and (3) an advice-following diffusion policy refined via Group Relative Policy Optimization under a multi-objective reward that ensures safety, comfort, and alignment with semantic intent. We evaluate our method on the NAVSIM benchmark in a closed-loop setting, achieving a state-of-the-art Predictive Driver Model Score (PDMS) of 91.5, outperforming strong baselines in safety (NC: 99.2). The results demonstrate that leveraging language as a cognitive interface between perception and control enhances both generalization and behavioral transparency, advancing the paradigm of language-conditioned driving.

The purely visual, multi-view perception approach provides a cost-effective solution for autonomous driving perception. However, vision-based systems struggle to achieve the same precision in object localization as LiDAR due to fundamental differences in their sensing mechanisms. To address this, we introduce MDC, a novel method that integrates LiDAR’s superior spatial information into camera-based systems. Our approach includes three key distillation modules: Distribution Consistency Distillation (DCD), Mask Adaptive Distillation (MAD), and Result Distillation (RD). DCD aligns point cloud and multi-view voxel distributions to boost 3D spatial perception. MAD uses adaptive masking to refine BEV feature alignment with LiDAR. RD ensures consistency in the decoding phase. Experiments conducted on the nuScenes benchmark demonstrate that our method achieves a performance improvement of 2.7% to 3.4% over the student network, highlighting its potential to enhance autonomous driving perception capabilities.

Recent efforts have been increasingly made on vehicle reidentification (re-ID), which has huge contributions to intelligent transportation and criminal investigation. However, most existing methods heavily rely on the color and texture features of vehicles to discern their identities, which turn invalid under adversarial social security occasions where vehicles' color and style are always tampered or forged by crime suspects. In this paper, we propose a local feature preservation method to learn the structure-aware features from the position distribution of individual local regions within vehicle front window area, which appears more robust and discriminative upon disguise. We further develop a two-branch deep convolutional network framework to integrate the structure-aware features with vehicle model features for vehicle Re-ID. The experimental results on datasets VehicleID and Vehicle-1M show that our end-to-end framework achieves promising performance and outperforms the state-of-the-art methods proposed so far.

Vehicle re-identification (re-ID) has received more attention in recent years as a significant work, making huge contribution to the intelligent video surveillance. The complex intra-class and inter-class variation of vehicle images bring huge challenges for vehicle re-ID, especially for the similar vehicle re-ID. In this paper we focus on an interesting and challenging problem, vehicle re-ID of the same/similar model. Previous works mainly focus on extracting global features using deep models, ignoring the individual loa-cal regions in vehicle front window, such as decorations and stickers attached to the windshield, that can be more discriminative for vehicle re-ID. Instead of directly embedding these regions to learn their features, we propose a Regional Structure-Aware model (RSA) to learn structure-aware cues with the position distribution of individual local regions in vehicle front window area, constructing a FW structural map space. In this map sapce, deep models are able to learn more robust and discriminative spatial structure-aware features to improve the performance for vehicle re-ID of the same/similar model. We evaluate our method on a large-scale vehicle re-ID dataset Vehicle-1M. The experimental results show that our method can achieve promising performance and outperforms several recent state-of-the-art approaches.

In this paper, an inverse method was developed which can, in principle, reconstruct arbitrary permeability, conductivity, thickness, and lift-off with a multifrequency electromagnetic sensor from inductance spectroscopic measurements. Both the finite-element method and the Dodd and Deeds formulation are used to solve the forward problem during the inversion process. For the inverse solution, a modified Newton-Raphson method was used to adjust each set of parameters (permeability, conductivity, thickness, and lift-off) to fit inductances (measured or simulated) in a least-squared sense because of its known convergence properties. The approximate Jacobian matrix (sensitivity matrix) for each set of the parameter is obtained by the perturbation method. Results from an industrial-scale multifrequency sensor are presented including the effects of noise. The results are verified with measurements and simulations of selected cases. The findings are significant because they show for the first time that the inductance spectra can be inverted in practice to determine the key values (permeability, conductivity, thickness, and lift-off) with a relative error of less than 5% during the thermal processing of metallic plates.

This paper presents a novel approach to background subtraction which aims to extract moving objects in video stream. To this end, a novel background model is proposed by using both working backgrounds and candidate backgrounds, which can be transferred to each other according to an adaptive mechanism. The input image (video frame) is compared and evaluated with these dual-class backgrounds (DCB) to detect foreground objects. Furthermore, for robust background modeling a novel background updating scheme is proposed based on the life-value which represents the existing time of a background sample, and the access-time which represents the number of valid visits of a background sample. Experiments on a standard dataset demonstrated the effectiveness and robustness of the proposed approach by comparing it with the previous typical background subtraction techniques.