arXiv subjects

Manjiang Hu

Publications and source records attributed to Manjiang Hu.

4 recordsLinked to original sources

arXiv2025

Multimodal Classification Network Guided Trajectory Planning for Four-Wheel Independent Steering Autonomous Parking Considering Obstacle Attributes

Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, leading to suboptimal efficiency or planning failures. To address this issue, we propose a novel multimodal trajectory planning framework that employs a neural network for scene perception, combines 4WIS hybrid A* search to generate a warm start, and utilizes an optimal control problem (OCP) for trajectory optimization. Specifically, a multimodal perception network fusing visual information and vehicle states is employed to capture semantic and contextual scene understanding, enabling the planner to adapt the strategy according to scene complexity (hard or easy task). For hard tasks, guided points are introduced to decompose complex tasks into local subtasks, improving the search efficiency. The multiple steering modes of 4WIS vehicles, Ackermann, diagonal, and zero-turn, are also incorporated as kinematically feasible motion primitives. Moreover, a hierarchical obstacle handling strategy, which categorizes obstacles as "non-traversable", "crossable", and "drive-over", is incorporated into the node expansion process, explicitly linking obstacle attributes to planning actions to enable efficient decisions. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in OCP. Experimental results demonstrate the proposed framework's effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.

cs.RO↗

arXiv2025

LTMSformer: A Local Trend-Aware Attention and Motion State Encoding Transformer for Multi-Agent Trajectory Prediction

It has been challenging to model the complex temporal-spatial dependencies between agents for trajectory prediction. As each state of an agent is closely related to the states of adjacent time steps, capturing the local temporal dependency is beneficial for prediction, while most studies often overlook it. Besides, learning the high-order motion state attributes is expected to enhance spatial interaction modeling, but it is rarely seen in previous works. To address this, we propose a lightweight framework, LTMSformer, to extract temporal-spatial interaction features for multi-modal trajectory prediction. Specifically, we introduce a Local Trend-Aware Attention mechanism to capture the local temporal dependency by leveraging a convolutional attention mechanism with hierarchical local time boxes. Next, to model the spatial interaction dependency, we build a Motion State Encoder to incorporate high-order motion state attributes, such as acceleration, jerk, heading, etc. To further refine the trajectory prediction, we propose a Lightweight Proposal Refinement Module that leverages Multi-Layer Perceptrons for trajectory embedding and generates the refined trajectories with fewer model parameters. Experiment results on the Argoverse 1 dataset demonstrate that our method outperforms the baseline HiVT-64, reducing the minADE by approximately 4.35%, the minFDE by 8.74%, and the MR by 20%. We also achieve higher accuracy than HiVT-128 with a 68% reduction in model size.

cs.CV↗

arXiv2025

Observer-Based Distributed Model Predictive Control for String-Stable Multi-vehicle Systems with Markovian Switching Topology

Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this study, we propose an observer-based distributed model predictive control (DMPC) method for vehicle platoons under directed Markovian switching topologies. Considering the stochastic nature of the switching topologies, we model the directed switching communication topologies using a continuous-time Markov chain. To obtain the leader vehicle's information for controller design, we develop a fully distributed adaptive observer that can quickly adapt to the randomly switching topologies, ensuring that the observed information is not affected by the dynamic topology switches. Additionally, a sufficient condition is derived to guarantee the mean-square stability of the observer. Furthermore, we construct the DMPC terminal update law based on the observer and formulate a string stability constraint based on the observed information. Numerical simulations demonstrate that our method can reduce tracking errors while ensuring string stability.

eess.SY↗

arXiv2020

Distributed Model Predicted Control of Multi-agent Systems with Applications to Multi-vehicle Cooperation

This paper proposes a distributed model predicted control (DMPC) approach for consensus control of multi-agent systems (MASs) with linear agent dynamics and bounded control input constraints. Within the proposed DMPC framework, each agent exchanges assumed state trajectories with neighbors and solves a local open-loop optimization problem to obtain the optimal control input. In the optimization problem, a discrete-time consensus protocol is introduced into update law design for assumed terminal states, with which asymptotic consensus of assumed terminal states and recursive feasibility are rigorously proved. Together with the optimal cost function, an infinite series of cost-to-go functions is introduced into the design of a Lyapunov function, with which closed-loop asymptotic consensus is finally proved. Two applications including cooperation of autonomous underwater vehicles (AUVs) and connected and automated vehicles (CAVs) are used to validate the effectiveness of the proposed DMPC approach.

eess.SY↗