Trajectory planning involving multi-agent interactions has been a long-standing challenge in the field of robotics, primarily burdened by the inherent yet intricate interactions among agents. While game-theoretic methods are widely acknowledged for their effectiveness in managing multi-agent interactions, significant impediments persist when it comes to accommodating the intentional uncertainties of agents. In the context of intentional uncertainties, the heavy computational burdens associated with existing game-theoretic methods are induced, leading to inefficiencies and poor scalability. In this paper, we propose a novel game-theoretic interactive trajectory planning method to effectively address the intentional uncertainties of agents, and it demonstrates both high efficiency and enhanced scalability. As the underpinning basis, we model the interactions between agents under intentional uncertainties as a static Bayesian game, and we show that its agent-form equivalence can be represented as a potential game under certain assumptions. The existence and attainability of the optimal interactive trajectories are illustrated, as the corresponding static Bayesian Nash equilibrium can be attained by optimizing a unified optimization problem. Additionally, we present a distributed algorithm based on the dual consensus alternating direction method of multipliers (ADMM) tailored to the parallel solving of the problem, thereby significantly improving the scalability. The attendant outcomes from simulations and experiments demonstrate that the proposed method is effective across a range of scenarios characterized by general forms of intentional uncertainties. Its scalability surpasses that of existing centralized and decentralized baselines, allowing for real-time interactive trajectory planning in uncertain game settings. The source code will be available onhttps://github.com/zhuangdf/Potential-Bayesian-Game-release.

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub.

To address the intricate challenges of cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a novel cooperative driving framework that leverages a Large Multimodal Model (LMM) to improve traffic efficiency and passenger experience in dynamic urban environments. This framework seamlessly integrates scheduling and motion planning processes to ensure the effective operation of Cooperative Autonomous Vehicles (CAVs). The spatial relationship between CAVs and passenger requests is abstracted into a Bird’s-Eye View (BEV) image to fully exploit the potential of the multimodal understanding ability of LMMs. Besides, trajectories are cautiously refined for each CAV while ensuring collision avoidance through safety constraints. A decentralized optimization strategy, facilitated by the Alternating Direction Method of Multipliers (ADMM) within the LMM framework, is proposed to drive the graph evolution of CAVs. Simulation results in diverse urban scenarios demonstrate the pivotal role and significant impact of LMM in optimizing CAV scheduling and seamlessly serving a decentralized cooperative optimization process for each CAV. This marks a substantial stride towards practical, efficient, and safe AMoD systems that are poised to revolutionize urban transportation. The code is available at https://github.com/henryhcliu/LMMCoDrive.

Current autonomous driving systems often struggle to balance decision-making and motion control while ensuring safety and traffic rule compliance, especially in complex urban environments. Existing methods may fall short due to separate handling of these functionalities, leading to inefficiencies and safety compromises. To address these challenges, we introduce UDMC, an interpretable and unified Level 4 autonomous driving framework. UDMC integrates decision-making and motion control into a single optimal control problem (OCP), considering the dynamic interactions with surrounding vehicles, pedestrians, road lanes, and traffic signals. By employing innovative potential functions to model traffic participants and regulations, and incorporating a specialized motion prediction module, our framework enhances on-road safety and rule adherence. The integrated design allows for real-time execution of flexible maneuvers suited to diverse driving scenarios. High-fidelity simulations conducted in CARLA exemplify the framework’s computational efficiency, robustness, and safety, resulting in superior driving performance when compared against various baseline models. Our open-source project is available athttps://github.com/henryhcliu/udmc_carla.git.

The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. Difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving task of CAVs as an optimal control problem with safety conditions enforced as hard constraints, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs with the travel efficiency improved and the hard safety constraints satisfied. Specifically, all constraints involved are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, with an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence. It is noteworthy that the travel efficiency is enhanced and the computation burden is considerably alleviated with the proposed innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons to demonstrate the effectiveness and efficiency of the proposed approach.

Structure from Motion (SfM) and visual localization in indoor texture-less scenes and industrial scenarios present prevalent yet challenging research topics. Existing SfM methods designed for natural scenes typically yield low accuracy or map-building failures due to insufficient robust feature extraction in such settings. Visual markers, with their artificially designed features, can effectively address these issues. Nonetheless, existing marker-assisted SfM methods encounter problems like slow running speed and difficulties in convergence; and also, they are governed by the strong assumption of unique marker size. In this paper, we propose a novel SfM framework that utilizes planar markers and multiple cameras with known extrinsics to capture the surrounding environment and reconstruct the marker map. In our algorithm, the initial poses of markers and cameras are calculated with Perspective-n-Points (PnP) in the front-end, while bundle adjustment methods customized for markers and camera groups are designed in the back-end to optimize the 6-DOF pose directly. Our algorithm facilitates the reconstruction of large scenes with different marker sizes, and its accuracy and speed of map building are shown to surpass existing methods. Our approach is suitable for a wide range of scenarios, including laboratories, basements, warehouses, and other industrial settings. Furthermore, we incorporate representative scenarios into simulations and also supply our datasets with pose labels to address the scarcity of quantitative ground-truth datasets in this research field. The datasets and source code are available on GitHub1.

Cooperative trajectory planning of connected autonomous vehicles (CAVs) generally admits strong nonlinearity and non-convexity, rendering great difficulties in finding the optimal solution. Existing methods typically suffer from low computational efficiency and poor scalability, which hinder the appropriate applications in large-scale scenarios involving an increasing number of vehicles. To tackle this problem, we propose a novel decentralized iterative linear quadratic regulator (iLQR) algorithm by leveraging the dual consensus alternating direction method of multipliers (ADMM). First, the original non-convex optimization problem is reformulated into a series of convex optimization problems through iterative neighbourhood approximation. Then, the dual of each convex optimization problem is shown to have a consensus structure, which facilitates the use of consensus ADMM to solve for the dual solution in a fully decentralized and parallel architecture. Finally, the primal solution corresponding to the trajectory of each vehicle is recovered by solving a linear quadratic regulator (LQR) problem iteratively, and a novel trajectory update strategy is proposed to ensure the dynamic feasibility of vehicles. With the proposed development, the computation burden is significantly alleviated such that real-time performance is attainable. Two traffic scenarios are presented to validate the proposed algorithm, and thorough comparisons between our proposed method and baseline methods (including centralized iLQR, IPOPT, and SQP) are conducted to demonstrate the scalability of the proposed approach.