DriveGPT: Scaling Autoregressive Behavior Models for Driving

• URL: http://arxiv.org/abs/2412.14415v2
• Date: Wed, 12 Feb 2025 05:41:42 GMT
• Title: DriveGPT: Scaling Autoregressive Behavior Models for Driving
• Authors: Xin Huang, Eric M. Wolff, Paul Vernaza, Tung Phan-Minh, Hongge Chen, David S. Hayden, Mark Edmonds, Brian Pierce, Xinxin Chen, Pratik Elias Jacob, Xiaobai Chen, Chingiz Tairbekov, Pratik Agarwal, Tianshi Gao, Yuning Chai, Siddhartha Srinivasa,
• Abstract summary: We present DriveGPT, a scalable behavior model for autonomous driving. We learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties.
• Score: 11.733428769776204
• License:
• Abstract: We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute. We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling.

Related papers

• DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers [61.92571851411509]
  We introduce a multimodal driving language based on interleaved image and action tokens, and develop DrivingGPT to learn joint world modeling and planning. Our DrivingGPT demonstrates strong performance in both action-conditioned video generation and end-to-end planning, outperforming strong baselines on large-scale nuPlan and NAVSIM benchmarks.
  arXiv  Detail & Related papers  (2024-12-24T18:59:37Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving [15.965681867350215]
  This paper introduces a trajectory prediction model tailored for autonomous driving. It harnesses historical trajectory data combined with a novel geometric dynamic graph-based behavior-aware module. It achieves computational efficiency and reduced parameter overhead.
  arXiv  Detail & Related papers  (2024-05-02T13:13:52Z)
• SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control [59.20038082523832]
  We present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We develop a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data.
  arXiv  Detail & Related papers  (2024-03-28T14:07:13Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Fully End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-Agent [2.512827436728378]
  We propose a novel deep learning model trained with end-to-end and multi-task learning manners to perform both perception and control tasks simultaneously. The model is evaluated on CARLA simulator with various scenarios made of normal-adversarial situations and different weathers to mimic real-world conditions.
  arXiv  Detail & Related papers  (2022-04-12T03:57:01Z)
• Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation [11.007092387379076]
  We develop and train take-over time (TOT) models that operate on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views. We show that a TOT model supported by augmented data can be used to produce continuous estimates of take-over times without delay.
  arXiv  Detail & Related papers  (2021-07-27T16:39:50Z)

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.