Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model

arXiv:2210.04017v3 Announce Type: replace
Abstract: End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals. Recent advanced methods construct a latent world model to map the high dimensional observations into compact latent space. However, the latent states embedded by the world model proposed in previous works may contain a large amount of task-irrelevant information, resulting in low sampling efficiency and poor robustness to …

abstract advanced arxiv autonomous autonomous driving construct control cs.lg driving efficiency map mapping performance pixels raw robustness sample semantic type urban via world world model