Autonomous driving technology has achieved significant breakthroughs in open scenarios, enabling the deployment of excellent positioning, detection, and navigation algorithms on passenger vehicles. However, there has been limited research attention devoted to autonomous driving for specialized vehicles in non-open scenarios. This manuscript introduces a perception system designed for heavy-duty mining transportation trucks operating in open-pit mines, which are typical of non-open scenarios. The system comprises four independent algorithms: high-precision fusion positioning, multi-task 2D detection, 9 Degrees of Freedom (9 DoF) 3D head, and autonomous navigation technology. Experimental verification demonstrates the effectiveness of these methods in addressing the challenges posed by mining environments, ultimately leading to enhanced safety and efficiency for trucks. This research outcome, through the comprehensive examination of positioning, detection, and navigation, aims to address the challenges encountered by mining trucks during operations. Its significance lies in enhancing automation levels in mining scenarios.

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

To address the challenges of low overlap and viewing direction differences in large-scale roadside LiDAR calibration, this paper proposes RoCalib, a novel automatic roadside LiDAR calibration method based on the high-definition (HD) map. This method enables geographic registration of the LiDAR without any specific target and improves the efficiency and safety of the large-scale roadside facility calibration and maintenance. First, a novel virtual reprojection model is designed to construct a virtual mapping from the HD map to LiDAR, reducing representation differences. Based on this, a universal spatial context descriptor is introduced, applicable to various LiDAR systems, facilitating rapid retrieval of LiDAR positions within the HD map. Finally, based on the multi-feature optimization method considering the road structure, the fine registration and parameter calibration of the roadside LiDAR and the HD map are completed. The proposed framework is validated on simulated, public, and self-collected datasets, demonstrating that this method can automatically and accurately achieve multi-LiDAR geographic calibration, yielding superior performance.

Autonomous exploration of unmanned ground vehicles (UGVs) is a critical yet challenging task that has garnered widespread attention and experienced rapid progress. In exploration tasks, redundant revisits to known areas frequently occur, significantly impacting exploration efficiency. To address this issue, we introduce a multi-representation exploration strategy with a long-short path approach. First, we use topological and terrain maps to represent local frontiers. Topological maps enhance the sensor’s spatial perception capabilities, while terrain maps provide detailed map expressions. Together, they improve exploration efficiency. Second, the long-short path strategy is applied to update the global topology, making its construction more robust. Third, our algorithm incorporates topological information into the decision-making process, enhancing both the efficiency and coherence of exploration. In our experimental evaluations, our algorithm was benchmarked against mainstream algorithms and demonstrated superior performance across various indicators. Compared to other algorithms, our algorithm is at least 4.54% faster in total time and reduces the total traveling distance by 4.82%.

Collaborative perception technology improves perception performance by enabling agents to exchange complementary perceptual data through the sharing and fusion of multi-viewpoint information. However, existing collaborative perception methods in V2X scenarios face two main challenges. On one hand, they overly rely on sensor observation data for global perception, resulting in an exponential increase in communication bandwidth demands as the scene complexity grows. On the other hand, existing methods treat the trajectory and location information of traffic participants separately from sensor data, neglecting the fact that vehicles, as intelligent agents, serve both as sources and targets of perception. This limitation constrains the further improvement of collaborative perception performance. To address these issues, this paper proposes a novel position-prior-enhanced collaborative perception network, PosiFusion. In terms of communication, PosiFusion introduces a position-prior-based communication selection mechanism that uses prior location information to generate a confidence map of the global perception space. By selecting critical perceptual areas, it significantly reduces the communication bandwidth requirement. Regarding perception performance, PosiFusion incorporates a critical-area perception guidance module, which generates a guidance map of the global perception space using prior information. This guides the network to focus on the perception data from critical areas, thereby enhancing overall perception accuracy. To evaluate the effectiveness of PosiFusion, we conducted tests on two large-scale vehicular collaborative perception datasets, OPV2V and V2XSet. Experimental results demonstrate that PosiFusion outperforms existing state-of-the-art collaborative perception methods while ensuring minimal communication transmission costs.