High-frequency dynamic targets introduce substantial appearance variations in LiDAR scans of the same location over time, posing a major challenge for place recognition. To tackle this, we propose DyLPR, a cascaded PR framework that integrates a LiDAR depth inpainting network and a place recognition network (PTN-Net), leveraging the complementary strengths of convolutional neural networks (CNNs) and transformer architectures. Specifically, a supervised encoder–decoder combining CNNs and transformers is employed to effectively handle dynamic masks across multiple scales. A semantic auxiliary branch and a hybrid loss function are further introduced to enhance both structural consistency and texture fidelity, resulting in more accurate and realistic depth inpainting. PTN-Net employs a pyramidal convolutional backbone with parallel Transformer-NetVLAD modules to capture long-range multiscale dependencies and adaptively aggregate salient features, while context gating refines integration to improve descriptor compactness and discriminability. Extensive evaluations on the LiDAR depth inpainting dataset, constructed from benchmark SemanticKITTI and real-vehicle data, demonstrate that our method achieves competitive inpainting performance and outperforms existing approaches in place recognition under dynamic conditions. For instance, DyLPR improves Recall@1 by an absolute 5.4% over the best baseline.

Visual place recognition (VPR) improves the localization accuracy of agents in complex environments by extracting effective environmental representations for place matching, and it does not rely on additional high-precision, high-cost sensors or digital maps. However, current VPR research still faces limitations when simultaneously addressing the challenges of viewpoint changes and dynamic target interference, where it is not easy to obtain stable and reliable viewpoint-invariant environmental representations. To address these challenges, inspired by the human ability to recognize scenes, this paper customizes a place recognition network called VDI-Net based on multi-view images, immune to viewpoint changes and dynamic interference. Specifically, a dynamic target filtering module (DTF) is proposed to effectively filter out dynamic targets in complex environments. To tackle the challenge of viewpoint changes, a vision-surround Mamba module (VSM) is introduced to enhance the rotational invariance of features across different viewpoints. In the comparative validation of the nuScenes dataset and our real-vehicle collection dataset, the experimental results show that our proposed method achieves satisfactory performance in addressing the challenges of viewpoint changes and dynamic target interference. The proposed method outperforms current representative VPR baselines and surpasses some classical LiDAR-based and multimodal place recognition baselines.

Light detection and ranging (LiDAR) positioning has received great attention especially when satellites fail. However, the positioning accuracy is still subjected to the following. First, the LiDAR positioning accuracy is affected by the sparsity due to LiDAR beams and environment. Second, the existing methods for suppressing cumulative errors are based on ego-vehicle sensing that requires the motion of repeated paths. Moreover, the output frequency of LiDAR is low. To solve the above problems, an enhanced-LiDAR/ultrawideband (UWB)/inertial navigation system integrated positioning methodology is proposed. First, the enhanced LiDAR odometry module is designed to improve the resolution of LiDAR beams for more accurate odometry. Then, the cooperative optimization module is proposed to introduce UWB observation to suppress the accumulated error without relying on ego-vehicle sensing. Finally, the factor graph fusion module is used to fuse multisensor information dynamically and improve the output frequency. Experimental results prove the effectiveness of our methodology.

The dynamic interaction among Connected and Automated Vehicles (CAVs) is becoming increasingly complex, encompassing factors such as dynamic topology and the dynamic states of multiple CAVs. Existing cooperative control methods struggle to explicitly represent dynamic interaction, which can lead to dangerous behavior, severe congestion, and even accidents. In this paper, we propose a cooperative control methodology that aims to improve safety and efficiency in the merging zone by deeply representing dynamic interaction among multiple CAVs. Our proposed methodology, named GMA-DRL, utilizes a spatial graph convolutional encoder with a multi-head attention mechanism to explicitly represent dynamic interaction among vehicles. Furthermore, deep reinforcement learning based on Actor-Critic with temporal relation regularization is utilized to ensure the consistency of dynamic interaction and generate cooperative driving actions for multiple CAVs. The GMA-DRL is tested in a series of typical merging scenarios with dynamic interaction. Extensive experimental results show that the GMA-DRL outperforms the existing cooperative control models in term of headway, average speed and acceleration. It demonstrates that the GMA-DRL with explicitly represent dynamic interaction can improve safety and efficiency of multiple CAVs in the merging zone.

Place recognition plays a crucial role in simultaneous localization and mapping. Unfortunately, however, changes in viewpoints and conditions in large-scale environments impose tricky challenges for PR. To this end, this article specifically proposes an explicit points-of-interest driven PR method, which consists of a road segmentation module based on grid-wise patch U-transformer and a PR module based on regions of interest siamese transformer NetVLAD (RI_STV). Especially for RI_STV, in the individual dimension, it is dedicated to exploring the local topological features of nonroad regions of interest. In the spatial dimension, an improved Transformer is introduced to capture the global interactions between features of interest. In the cluster dimension, NetVLAD embedded with weighted pooling is created to perform weighted aggregation of feature clusters to generate discriminative and general descriptors. Evaluation on various datasets shows that our customized method is not only impressively competitive, but also strikes the best balance between accuracy and real-time performance.

The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view. Lacking of detailed driving recommendation, it ignores the safety of merging zone where commercial vehicle pose a high collision risk in real traffic. This paper proposes a roadside decision-making methodology to simultaneously improve the safety and efficiency of merging zone. We have built two modules, namely assessment and decision-making. Assessment module takes advantage of Bayesian inference to evaluate dynamic collision risk. Decision-making module based on deep reinforcement learning recommends the actions to commercial vehicles by roadside unit. A series of typical simulation tests show that our method increases the TTC of commercial vehicles by an average of 62.7%. In the free flow, the overall travel time of vehicles in the merging zone is reduced by 11.68%. Most notably, when congestion occurred, the average jam length is reduced by 59.68% on the premise of safety. Moreover, the average accuracy of the roadside decision-making method on the evaluation metrics of TTC, travel time, and jam length are 93.73%, 91.65%, and 94.45%, respectively. The experimental results show that the roadside decision-making methodology simultaneously improves safety and efficiency, and it dynamically adapts free and congested traffic flow.