73 citations · 94 across the 10 of their papers we have counts for
10 papers
Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions
Yueyuan Li, Rongcheng Nie, Weijie Xi +4
Game-theoretic models provide principled frameworks for modeling vehicle interactions, but their underlying temporal assumptions have not been systematically examined against real-…
RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation
Yueyuan Li, Zexi Chen, Weijie Xi +4
Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks. Existing approaches either rely on h…
A Pedestrian-Vehicle Interaction Benchmark and Annotation Framework for Unstructured Scenes via Uncalibrated Cameras
Haoyang Peng, Qian Hu, Songan Zhang +1
Predicting the interaction between pedestrian and vehicle is essential for autonomous driving safety in unstructured and semi-structured scenarios; however, this task is severely h…
AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models
Zijin Zhou, Songan Zhang
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Traffic Accident Detection (TAD) and Traffic Accident Understanding (TAU). However, existing studies m…
A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking
Mingyang Jiang, Yueyuan Li, Jiaru Zhang +2
The growing demand for parking has increased the need for automated parking planning methods that can operate reliably in confined spaces. In restricted and complex environments, h…
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey
Liewen Liao, Weihao Yan, Wang Xu +3
Learning-based 3D reconstruction has emerged as a transformative technique in autonomous driving, enabling precise modeling of environments through advanced neural representations.…
From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
Yueyuan Li, Mingyang Jiang, Songan Zhang +3
In recent years, end-to-end autonomous driving architectures have gained increasing attention due to their advantage in avoiding error accumulation. Most existing end-to-end autono…
HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
Mingyang Jiang, Yueyuan Li, Songan Zhang +3
Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due…
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Yueyuan Li, Wei Yuan, Songan Zhang +4
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving h…
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
Yueyuan Li, Songan Zhang, Mingyang Jiang +4
Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems. However, existing simulators…