From the 1 of 9 linked papers with an AI index.
9 papers
DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving
Zebin Xing, Yupeng Zheng, Qiang Chen +10
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for end-to-end autonomous driving by enabling unified reasoning across perception, language, and p…
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jiahao Liu, Zhongpu Xia, Shuai Tian +13
WALA is a framework that learns executable latent actions for robot manipulation by pretraining on both action‑labeled demonstrations and unlabeled videos, predicting future change…
Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
Junli Wang, Zhihua Hua, Xueyi Liu +7
Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric deviations from expert trajectories…
DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Pengxuan Yang, Yupeng Zheng, Deheng Qian +11
We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps…
Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
Linbo Wang, Yupeng Zheng, Qiang Chen +13
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world re…
TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning
Zebin Xing, Pengxuan Yang, Linbo Wang +12
Current autonomous driving systems often favor end-to-end frameworks, which take sensor inputs like images and learn to map them into trajectory space via neural networks. Previous…