21 citations · 21 across the 10 of their papers we have counts for
11 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 Stitch in Time Saves Nine: Preserving Policy Compatibility Under Perception Updates in End-to-End Autonomous Driving
Yueyuan Li, Yifei Xiao, Mingyang Jiang +3
End-to-end autonomous driving systems tightly couple perception and decision-making through latent representations. Consequently, updates to perception models can alter these repre…
End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera
Jiale Zhang, Yeqiang Qian, Tong Qin +3
The increase in vehicle ownership has led to increased traffic congestion, more accidents, and higher carbon emissions. Vehicle platooning is a promising solution to address these…
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…
ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving
Yongming Chen, Miner Chen, Liewen Liao +5
Reinforcement learning (RL) in autonomous driving employs a trial-and-error mechanism, enhancing robustness in unpredictable environments. However, crafting effective reward functi…
Embodied Escaping: End-to-End Reinforcement Learning for Robot Navigation in Narrow Environment
Han Zheng, Jiale Zhang, Mingyang Jiang +4
Autonomous navigation is a fundamental task for robot vacuum cleaners in indoor environments. Since their core function is to clean entire areas, robots inevitably encounter dead z…
RL-OGM-Parking: Lidar OGM-Based Hybrid Reinforcement Learning Planner for Autonomous Parking
Zhitao Wang, Zhe Chen, Mingyang Jiang +2
Autonomous parking has become a critical application in automatic driving research and development. Parking operations often suffer from limited space and complex environments, req…
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…
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…