3 papers
cs.CV2026
MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
Ziying Song, Shengkai Zhang, Lin Liu +8
Long-horizon planning is critical for safe autonomous driving in complex scenarios. Existing methods improve planning continuity with temporal memory, but such memory may become in…
cs.RO2026
Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving
Yuchen Liu, Ziying Song, Shengkai Zhang +6
Reliable long-horizon planning remains a key challenge in end-to-end autonomous driving. By accounting for future motion evolution and potential consequences, it provides forward-l…
cs.CV2026
GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +8
End-to-end autonomous driving has made significant progress by unifying perception, prediction, and planning within a single learning framework, achieving strong performance in sho…