arXiv subjects

Wenxuan Ao

Publications and source records attributed to Wenxuan Ao.

4 recordsLinked to original sources

arXiv2026

M$^\text{4}$World: A Multi-view Multimodal Driving World Model for Interactive Object Manipulation and Minute-long Streaming

Driving-world generation has emerged as a core capability for scalable autonomous-driving simulation, yet existing methods remain limited in object-level controllability and long-horizon stability. We present M$^\text{4}$World, a Multi-view and Multimodal generative driving world model that synthesizes future surround-view video streams and synchronized LiDAR scans while supporting interactive object Manipulation and stable Minute-long streaming. Fine-grained object manipulation is realized through a flexible conditioning interface that supports explicit control over both the spatial layout and visual appearance of individual objects. Stable minute-long streaming, on the other hand, is achieved through a multi-stage training framework that enables online causal generation in only four denoising steps while maintaining coherent world dynamics throughout extended rollouts. Building on these components, we introduce an efficient few-clip post-training as well as a suite of visual reference-conditioned generation models, preserving general generation ability while allowing rare-case customization for long-tail controllability. To assess controllability beyond realism, we further introduce an automated VLM-based judging pipeline that evaluates scene-level condition adherence, view-wise object controllability, and cross-view object consistency. Comprehensive experiments show that M$^\text{4}$World consistently delivers high generation quality, precise controllability, and stable minute-long streaming. Together with downstream long-tail augmentation and scene editing, these results demonstrate the potential of M$^\text{4}$World for controllable, scalable driving simulation.

cs.CV↗

arXiv2025

CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control

City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particularly challenging. Given the prohibitive computational cost of learning individual policies for each intersection, some researchers explore learning a universal policy to control each intersection in a decentralized manner, where the key challenge is to construct a universal representation method for heterogeneous intersections. However, existing methods are limited to universally representing information of heterogeneous ego intersections, neglecting the essential representation of influence from their heterogeneous neighbors. Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor's influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. Extensive experiments on five city-scale datasets, ranging from 97 to 13,952 intersections, confirm the efficacy of CityLight, with an average throughput improvement of 11.68% and a lift of 22.59% for generalization.

eess.SY↗

arXiv2024

A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking

With the development of artificial intelligence techniques, transportation system optimization is evolving from traditional methods relying on expert experience to simulation and learning-based decision and optimization methods. Learning-based optimization methods require extensive interactions with highly realistic microscopic traffic simulators. However, existing microscopic traffic simulators are inefficient in large-scale scenarios and thus fail to support the adoption of these methods in large-scale transportation system optimization scenarios. In addition, the optimization scenarios supported by existing simulators are limited, mainly focusing on the traffic signal control. To address these challenges, we propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation and optimization. The simulator can iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with 2,464,950 vehicles compared to the best baseline CityFlow. Besides, it achieves a more realistic average road speeds simulated on real datasets by adopting the IDM model as the car-following model and the randomized MOBIL model as the lane-changing model. Based on it, we implement a set of microscopic and macroscopic controllable objects and metrics provided by Python API to support typical transportation system optimization scenarios. We choose five representative scenarios and benchmark classical rule-based algorithms, reinforcement learning algorithms, and black-box optimization algorithms in four cities. These experiments effectively demonstrate the usability of the simulator for large-scale traffic system optimization. The code of the simulator is available at https://github.com/tsinghua-fib-lab/moss. We build an open-registration web platform available at https://moss.fiblab.net to support no-code trials.

cs.AI↗

arXiv2024

MOSS: A Large-scale Open Microscopic Traffic Simulation System

In the research of Intelligent Transportation Systems (ITS), traffic simulation is a key procedure for the evaluation of new methods and optimization of strategies. However, existing traffic simulation systems face two challenges. First, how to balance simulation scale with realism is a dilemma. Second, it is hard to simulate realistic results, which requires realistic travel demand data and simulator. These problems limit computer-aided optimization of traffic management strategies for large-scale road networks and reduce the usability of traffic simulations in areas where real-world travel demand data are lacking. To address these problems, we design and implement MObility Simulation System (MOSS). MOSS adopts GPU acceleration to significantly improve the efficiency and scale of microscopic traffic simulation, which enables realistic and fast simulations for large-scale road networks. It provides realistic travel Origin-Destination (OD) matrices generation through a pre-trained generative neural network model based on publicly available data on a global scale, such as satellite imagery, to help researchers build meaningful travel demand data. It also provides a complete open toolchain to help users with road network construction, demand generation, simulation, and result analysis. The whole toolchain including the simulator can be accessed at https://moss.fiblab.net and the codes are open-source for community collaboration.

cs.DC↗