In recent years, Large Language Models (LLMs) have gained increasing attention in personalized recommendation systems due to their strong capabilities to understand contextual and semantic information. However, most existing LLM-based recommendation methods primarily rely on textual features, often neglecting the unique structural information embedded in user and item IDs, which are critical for capturing personalized preferences. To bridge this gap, we propose a novel method called CL 3M - Rec, which enhances the semantic representation of user and item IDs through Contrastive Learning (CL) and optimizes LLM recommendation performance via prompt construction. By effectively integrating structured ID information with textual semantics, our approach enables the LLMs to better model individual user interests and improve personalized prediction. Experiments on four public datasets demonstrate that CL3M-Rec consistently outperforms existing LLM-based and CL-based recommendation approaches, achieving superior predictive accuracy and robust generalization across diverse recommendation scenarios. The code of our method is available at https://github.com/Lewis44zhou/CL-3M-Rec.

Autonomous driving datasets have played an important role in validating the advancement of intelligent vehicle algorithms including localization, perception and prediction in academic areas. However, current existing datasets pay more attention to the structured urban road, which hampers the exploration on unstructured special scenarios. Moreover, the open-pit mine is one of the typical representatives for them. Therefore, we introduce the Autonomous driving dataset on the Mining scene (AutoMine) for positioning and perception tasks in this paper. The AutoMine is collected by multiple acquisition platforms including an SUV, a wide-body mining truck and an ordinary mining truck, depending on the actual mine operation scenarios. The dataset consists of 18+ driving hours, 18K annotated lidar and image frames for 3D perception with various mines, time-of-the-day and weather conditions. The main contributions of the AutoMine dataset are as follows: I.The first autonomous driving dataset for perception and localization in mine scenarios. 2.There are abundant dynamic obstacles of 9 degrees of freedom with large dimension difference (mining trucks and pedestrians) and extreme climatic conditions (the dust and snow) in the mining area. 3.Multi-platform acquisition strategies could capture mining data from multiple perspectives that fit the actual operation. More details can be found in our website(https://automine.cc).

Precise segmentation of road areas using cheap Lidar is a tough and critical task due to data sparsity problem. With sparse point clouds, reliable perception of environment is difficult due to the lack of available information and loss of object features. This paper presents a new approach to use sparse 3D Lidar data for road segmentation by fusing multiple frames of point cloud. With registration of multiple frames into a same coordinate system, reliable data can be provided for later ground segmentation and edge extraction. The accuracies of extensive experiments on three kinds of roads demonstrate that the proposed approach obtains high precision and reliability.

Autonomous driving requires 3D representations of the environments as high definition maps. In many cases, it is not efficient for a single vehicle to map the entire large environment. Therefore, a group of vehicles could cooperate to build maps. In this paper, we propose an approach for cooperative 3D mapping by multiple vehicles working simultaneously as a team. Each vehicle uses 3D LIDAR sensor and local mapping algorithms to build local map and the global map can be obtained by merging all the local maps in an consistent manner. The challenges in cooperative mapping lie in both accuracy and efficiency. We show that our cooperative mapping approach can save mapping time as well as reduce the accumulated error often suffered by single vehicle mapping algorithms. Meanwhile, real world experiments results indicate that our mapping algorithm can be implemented online with minimum burden imposed on communication channel and computation resources on each vehicle.

Visual SLAM in low illumination scenes remains a considerably challenging task since the available amount of appearance information frequently stays insufficient. To tackle with this problem, we propose a novel SLAM framework by using both appearance information and thermal information, which possesses illumination-free recognizable contents, in a flexible manner. The key idea is to continuously update a RGB-T map, which contains both RGB and thermal map points to implement location and mapping. More specifically, in our SLAM system, we detect features in both RGB and thermal images and combine them together to update the RGB-T map and implement simultaneous location and mapping. Both quantitative and qualitative results demonstrate the effectiveness of our framework, especially under low illumination environments.

Scene flow is a key function of stereo-based environment perception system for mobile robotics and autonomous vehicle. Due to the heavy computing requirement and the limited computing resource, parallelized and embedded algorithms become quite important for the application of the mobile robotics. This paper develops a cross-platform embedded scene flow algorithm by using a coarse-grained software pipeline and OpenCL programming language. Our OpenCL algorithm is tested on 10 video streams from different datasets with different scenarios on different commercial-off-the-shelf (COTS) hardware. The average frame rates for the 10 videos can reach about 50 fps on both GPU and mobile device. The peak frame rates for certain videos on GPU can reach almost 450 fps. We also demonstrate that the COTS platform can provide sufficient computing power for stereo-based perception algorithm potentially by using OpenCL programming.

In spatial time division multiple access wireless mesh networks, not all links can be activated simultaneously, as links scheduled for transmission must satisfy the specified SINR requirements. Previously, slot assignment has been done on a link basis, where a set of links is selected for transmission in a given slot. However, if selected links are in deep fade or have no traffic to transmit, the slot is wasted. Thus, a node-based scheme was proposed, where a set of nodes is selected for transmission. Which link to be used by a node depends on the links' instantaneous traffic load. Although this allows us to exploit multi-user diversity, it creates a planning discrepancy: slot assignment is designed based on long-term channel statistics, but scheduling on short-term channel fading conditions. Consequently, the performance gain of the node-based scheme is not consistent: it is marginal under certain scenarios. To avoid the design discrepancy, we develop a new slot-assignment and routing framework in this paper. The new approach incorporates short-term channel fading statistics to optimize the long term slot assignment, routing and scheduling simultaneously. Hence, multi-user diversity can be exploited more efficiently. Not only is the performance gain of the resulting system significant (can be as much as 64% higher throughput than the scheme introduced by Chen and Lea), it is also less topology dependent compared with the one by Chen and Lea.