Publications

2024

1. 

   MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem

   Mingkai Tang, Yuanhang Li, Hongji Liu, Yingbing Chen, Ming Liu, and Lujia Wang

   the 33rd International Joint Conference on Artificial Intelligence (IJCAI) , 2024

   Abs

   With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. Some previous methods have been proposed to solve the MG-MAPF problem based on Decoupling the goal Vertex visiting order search and the Single-agent pathfinding (DVS). However, this paper demonstrates that the methods based on DVS cannot always obtain the optimal solution. To obtain the optimal result, we propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding (DSS). Additionally, we present the Time-Interval-Space Forest (TIS Forest) to enhance the efficiency of MGCBS by maintaining the shortest paths from any start point at any start time step to each safe interval at the goal points. The experiment demonstrates that our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.

2. 

   A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots

   Ren Xin, Hongji Liu, Yingbing Chen, Sheng Wang, and Ming Liu

   Under Review , 2024

   Abs PDF

   With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. Some previous methods have been proposed to solve the MG-MAPF problem based on Decoupling the goal Vertex visiting order search and the Single-agent pathfinding (DVS). However, this paper demonstrates that the methods based on DVS cannot always obtain the optimal solution. To obtain the optimal result, we propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding (DSS). Additionally, we present the Time-Interval-Space Forest (TIS Forest) to enhance the efficiency of MGCBS by maintaining the shortest paths from any start point at any start time step to each safe interval at the goal points. The experiment demonstrates that our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.

3. 

   VHDMap-SE: A Universal Vectorized High-definition Map Aided Vehicle State Estimator

   Hongji Liu, Mingkai Tang, Mingkai Jia, Yingbing Chen, Jin Wu and Ming Liu

   IEEE Transactions on Intelligent Vehicles (T-IV) , 2024

   Abs PDF

   As unmanned ground vehicles (UGVs) applications expand to large-scale and open road scenarios, vectorized high-definition maps (VHD maps) demonstrate greater potential in solving state correction problems than traditional metric maps. Previous related studies typically employ proprietary versions of VHD maps without fully leveraging the common traffic elements' information. In addition, there is a lack of research on efficient interaction methods between UGVs and VHD maps. To fill these gaps, we propose a universal UGV state estimation system and a query-based VHD map data exchange protocol. The system utilizes VHD maps to correct the lateral and longitudinal positions as well as the yaw orientation of UGVs. The data exchange protocol enables UGVs to obtain real-time VHD map information and process it efficiently. To ensure universality, we accommodate two widely used VHD map formats, ASAM OpenDRIVE and Apollo OpenDRIVE and provide corresponding map parsing methods. The evaluation of the system is conducted both in simulated and real-world scenes. In the simulation experiments, we fully measure the effectiveness and accuracy of our method, as well as its sensitivity to measurement noise. In real-world experiments, we compare the state estimation accuracy of our system with SOTA simultaneous localization and mapping methods on an open road. The results show that our system demonstrates better accuracy than other baselines on most data sequences. The proposed map data exchange protocol meets real-time requirements. For the community's reference, the system code is available at https://github.com/liuhj86/VHDMap-SE.

4. 

   PGO-IPM: Enhance IPM Accuracy with Pose-guided Optimization for Low-cost High-definition Angular Marking Map Generation

   Hongji Liu, Linwei Zheng, Xiaoyang Yan, Zhenhua Xu, Bohuan Xue, Yang Yu, and Ming Liu

   the 35th IEEE Intelligent Vehicles Symposium (IV) , 2024

   Abs PDF Website

   High-definition angular marking maps (HDAM maps) are vital in large-scale environments with variable appearances. In these scenarios, unmanned ground vehicles (UGVs) can use angular markings for localization because they are easy to identify and informative for localization. However, creating such a marking map relies heavily on manual measurement and annotation, which is time-consuming and laborious. Although Inverse Perspective Mapping (IPM) offers a low-cost and automated alternative, its accuracy is compromised by vehicle motion and the arduous pre-calibration of the IPM matrix. To fill these gaps, we propose a pose-guided optimization framework for IPM. This framework enables the automated generation of HDAM maps, while concurrently refining the preliminary IPM matrix. We deployed the proposed method in two different automated ports, and the method yielded HDAM maps with near-centimeter precision. Moreover, the refined IPM matrix matched the accuracy of manual calibrations. The supplementary materials and videos are available at http://liuhongji.site/PGO-IPM/.

5. 

   DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques

   Tianshuai Hu, Jianhao Jiao, Yucheng Xu, Hongji Liu, Sheng Wang, and Ming Liu

   Under Review , 2024

   Abs PDF

   Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels through object label comprehension and contextual information. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping.

6. 

   IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

   Yingbing Chen, Jie Cheng, Lu Gan, Sheng Wang, Hongji Liu, Xiaodong Mei, and Ming Liu

   IEEE Transactions on Intelligent Transportation Systems (T-ITS) , 2024

   Abs PDF

   Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still employ a uniform treatment of prediction outcomes and solely rely on collision-avoidance strategies, leading to suboptimal planning performance. To address this limitation, this paper presents a novel prediction-based interactive planning framework for autonomous driving. Our method incorporates interaction reasoning into spatio-temporal (s-t) planning by defining interaction conditions and constraints. Specifically, it records and continually updates interaction relations for each planned state throughout the forward search. We assess the performance of our approach alongside state-of-the-art methods in the CommonRoad environment. Our experiments include a total of 232 scenarios, with variations in the accuracy of prediction outcomes, modality, and degrees of planner aggressiveness. The experimental findings demonstrate the effectiveness and robustness of our method. It leads to a reduction of collision times by approximately 17.6% in 3-modal scenarios, along with improvements of nearly 7.6% in distance completeness and 31.7% in the fail rate in single-modal scenarios. For the community’s reference, our code is accessible at https://github.com/ChenYingbing/IR-STP-Planner.

7. 

   Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle “Snow Lion”

   Yingbing Chen, Jie Cheng, Sheng Wang, Hongji Liu, Xiaodong Mei, Xiaoyang Yan, Mingkai Tang, Ge Sun, Ya Wen, Junwei Cai, Xupeng Xie, Lu Gan, Mandan Chao, Ren Xin, Ming Liu, Jianhao Jiao, and Lujia Wang

   Under Review , 2024

   Abs PDF

   The rapid evolution of autonomous vehicles (AVs) has significantly influenced global transportation systems. In this context, we present ``Snow Lion'', an autonomous shuttle meticulously designed to revolutionize on-campus transportation, offering a safer and more efficient mobility solution for students, faculty, and visitors. The primary objective of this research is to enhance campus mobility by providing a reliable, efficient, and eco-friendly transportation solution that seamlessly integrates with existing infrastructure and meets the diverse needs of a university setting. To achieve this goal, we delve into the intricacies of the system design, encompassing sensing, perception, localization, planning, and control aspects. We evaluate the autonomous shuttle's performance in real-world scenarios, involving a 1146-kilometer road haul and the transportation of 442 passengers over a two-month period. These experiments demonstrate the effectiveness of our system and offer valuable insights into the intricate process of integrating an autonomous vehicle within campus shuttle operations. Furthermore, a thorough analysis of the lessons derived from this experience furnishes a valuable real-world case study, accompanied by recommendations for future research and development in the field of autonomous driving.