5 papers · 1 filter
EvoAD: Self-Evolving Autonomous Driving via Latent World-Model Guided Post-hoc Reflection
Junjie Yang, Qiming Zhang, Yuyan Li +12
End-to-end autonomous driving systems have achieved rapid progress by learning unified policies that map sensor observations directly to driving trajectories. However, once deploye…
From Likelihood to Utility: Quality-Axis Diffusion for End-to-End Autonomous Driving
Junjie Yang, Ziyi Shi, Shuqi Shen +3
Diffusion models have become a dominant paradigm for end-to-end autonomous driving, yet existing planners organize trajectory generation along the Gaussian-noise axis, treating dri…
EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving
Junjie Yang, Qingwei Zeng, Youyou Li +5
World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinctio…
A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles
Shuqi Shen, Junjie Yang, Hongliang Lu +3
Accurate and interpretable motion planning is essential for autonomous vehicles (AVs) navigating complex and uncertain environments. While recent end-to-end occupancy prediction me…
Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networks
Shuqi Shen, Junjie Yang, Hui Zhong +3
Accurate and real-time prediction of surrounding vehicles' lane-changing intentions is a critical challenge in deploying safe and efficient autonomous driving systems in open-world…