5 papers
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…
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…
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…