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.

Related papers

• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Autonomous Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving. Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner. Experiments conducted on nuScenes dataset demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• UnO: Unsupervised Occupancy Fields for Perception and Forecasting [33.205064287409094]
  Supervised approaches leverage annotated object labels to learn a model of the world. We learn to perceive and forecast a continuous 4D occupancy field with self-supervision from LiDAR data. This unsupervised world model can be easily and effectively transferred to tasks.
  arXiv  Detail & Related papers  (2024-06-12T23:22:23Z)
• Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving? [84.17711168595311]
  End-to-end autonomous driving has emerged as a promising research direction to target autonomy from a full-stack perspective. nuScenes dataset, characterized by relatively simple driving scenarios, leads to an under-utilization of perception information in end-to-end models. We introduce a new metric to evaluate whether the predicted trajectories adhere to the road.
  arXiv  Detail & Related papers  (2023-12-05T11:32:31Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• End-to-end Autonomous Driving: Challenges and Frontiers [45.391430626264764]
  We provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. We discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.
  arXiv  Detail & Related papers  (2023-06-29T14:17:24Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Exploring Attention GAN for Vehicle Motion Prediction [2.887073662645855]
  We study the influence of attention in generative models for motion prediction, considering both physical and social context. We validate our method using the Argoverse Motion Forecasting Benchmark 1.1, achieving competitive unimodal results.
  arXiv  Detail & Related papers  (2022-09-26T13:18:32Z)
• End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving [16.340715765227475]
  We propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed.
  arXiv  Detail & Related papers  (2021-04-18T14:05:18Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.