The coordinated control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) offers significant advantages in applications such as surveillance, navigation, and emergency response. Effective path planning is essential in such missions, especially in complex environments where UAVs must relay accurate environmental data to assist UGVs. However, in urban environments and disaster zones, wireless communication is often unstable due to severe interference and non-line-of-sight conditions, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework specifically designed to enhance the reliability for UAV/UGV cooperative path planning under unreliable wireless conditions. SemCom transmits only key information for path planning, reducing transmission volume without sacrificing accuracy. Based on this framework, a SemCom transceiver is designed to fulfill the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.

Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating surface normal maps from LiDAR data, multimodal methods are not suitable for real-time applications, particularly in real-world scenarios where higher FPS is required compared to slow navigation. This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data and its computational demands. Specifically, we utilize a pre-trained Vision Transformer (ViT) to extract rich features from RGB images. Additionally, we design a lightweight yet efficient decoder, which together improve both precision and inference speed. ROD establishes a new SOTA on ORFD and RELLIS-3D datasets, as well as an inference speed of 50 FPS, significantly outperforming prior models. Our code will be available at https://github.com/STLIFE97/offroad_roadseg.

In unstructured environments with complex obstacles and obscure road boundaries, the local planner faces more severe challenges in terms of safety and real-time performance. In order to fulfill these emerging requirements, we propose a novel Timed-Elastic-Band approach for unstructured environments, abbreviated as TEB-U. This approach incorporates a free space extraction optimization module for 2D occupancy grid maps, which efficiently transforms irregular free space boundaries into polygons and restrains robots within the boundaries. Moreover, a dynamic global point adjustment module is designed to adaptively correct the trajectory points obtained from the global planner, thereby enabling robots to travel along the centerline of free space and providing a better initial trajectory for subsequent modules. To reduce the computational cost, we replace the obstacle constraint of TEB with the boundary constraint in hyper-graph optimization. We evaluate our planner in three distinct scenarios, and the results show that TEB-U improves the average success rate by 21% and reduces the planning time by 23% compared to TEB in unstructured road, which demonstrates its safety and efficiency.

In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle’s surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal continuity of LiDAR data. Employing a tracking model to generate pseudo-ground-truths from a sequence of LiDAR frames, our method significantly augments the dataset, enhancing the model’s ability to learn on novel classes. However, this approach introduces a data imbalance biased to novel data that presents a new challenge of catastrophic forgetting. To mitigate this, we incorporate LoRA, a technique that reduces the number of trainable parameters, thereby preserving the model’s performance on base classes while improving its adaptability to novel classes. This work represents a significant step forward in few-shot 3D LiDAR semantic segmentation for autonomous driving. Our code is available at https://github.com/BowmanChow/Track-no-forgetting.

Large visual models have recently demonstrated their promising performance on zero-shot transfer. However, so far, none of the existing methods explicitly possess the ability to perform zero-shot transfer on parking-slot detection, which results in current deep-learning based methods relying on training datasets, and methods based on traditional computer vision exhibiting poor robustness. In this paper, we propose a large visual model-based parking-slot detection method, which utilizes a large visual model (segment anything) to segment an around-view image and infer parking-slots by analyzing the relationship of marking-points in masks. In addition, we classify real-world parking-slots into two categories, line-based and area-based. The proposed method employs a two-stage approach which has a manually designed post-processing step without training. Multiple experiments have been carried out on public benchmarks, and our method demonstrates the capability for zero-shot transfer. The code will be released at https://github.com/Zhai0123/SAM-PS.

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.