Related papers: Enhancing End-to-End Autonomous Driving with Latent World Model

Enhancing End-to-End Autonomous Driving with Latent World Model

URL: http://arxiv.org/abs/2406.08481v1
Date: Wed, 12 Jun 2024 17:59:21 GMT
Title: Enhancing End-to-End Autonomous Driving with Latent World Model
Authors: Yingyan Li, Lue Fan, Jiawei He, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang, Tieniu Tan,
Abstract summary: We propose a novel self-supervised method to enhance end-to-end driving without the need for costly labels. Our framework textbfLAW uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame. As a result, our approach achieves state-of-the-art performance in both open-loop and closed-loop benchmarks without costly annotations.
Score: 78.22157677787239
License: http://creativecommons.org/licenses/by/4.0/
Abstract: End-to-end autonomous driving has garnered widespread attention. Current end-to-end approaches largely rely on the supervision from perception tasks such as detection, tracking, and map segmentation to aid in learning scene representations. However, these methods require extensive annotations, hindering the data scalability. To address this challenge, we propose a novel self-supervised method to enhance end-to-end driving without the need for costly labels. Specifically, our framework \textbf{LAW} uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame. The predicted latent features are supervised by the actually observed features in the future. This supervision jointly optimizes the latent feature learning and action prediction, which greatly enhances the driving performance. As a result, our approach achieves state-of-the-art performance in both open-loop and closed-loop benchmarks without costly annotations.

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