Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs

• URL: http://arxiv.org/abs/2404.04869v1
• Date: Sun, 7 Apr 2024 08:31:12 GMT
• Title: Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
• Authors: Yiqun Duan, Qiang Zhang, Renjing Xu,
• Abstract summary: We propose a hybrid End-to-End learning framework for autonomous driving by combining basic driving imitation learning with Large Language Models. The proposed methodology can attain driving scores of 49.21%, coupled with an impressive route completion rate of 91.34% in the offline evaluation conducted via CARLA.
• Score: 10.812418229495506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature. However, a substantial proportion of existing research predominantly focuses on planning models for robotics that transmute the outputs derived from perception models into linguistic forms, thus adopting a `pure-language' strategy. In this research, we propose a hybrid End-to-End learning framework for autonomous driving by combining basic driving imitation learning with LLMs based on multi-modality prompt tokens. Instead of simply converting perception results from the separated train model into pure language input, our novelty lies in two aspects. 1) The end-to-end integration of visual and LiDAR sensory input into learnable multi-modality tokens, thereby intrinsically alleviating description bias by separated pre-trained perception models. 2) Instead of directly letting LLMs drive, this paper explores a hybrid setting of letting LLMs help the driving model correct mistakes and complicated scenarios. The results of our experiments suggest that the proposed methodology can attain driving scores of 49.21%, coupled with an impressive route completion rate of 91.34% in the offline evaluation conducted via CARLA. These performance metrics are comparable to the most advanced driving models.

Related papers

• Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach [6.913791588789051]
  We introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions. We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland. The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices.
  arXiv  Detail & Related papers  (2024-06-19T13:46:08Z)
• TasTe: Teaching Large Language Models to Translate through Self-Reflection [82.83958470745381]
  Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. We propose the TasTe framework, which stands for translating through self-reflection. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-12T17:21:21Z)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  This study focuses on the application of Multimodal Large Language Models (MLLMs) within the domain of autonomous driving. We evaluate the capability of various MLLMs as world models for driving from the perspective of a fixed in-car camera. Our results highlight a critical gap in the current capabilities of state-of-the-art MLLMs.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models [12.687494201105066]
  This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) to generate future motion from agents' past/observed trajectories and scene semantics. LLMs' powerful comprehension abilities capture a spectrum of high-level scene knowledge and interactive information. Emulating the human-like lane focus cognitive function, we introduce lane-aware probabilistic learning powered by the pioneering Mamba module.
  arXiv  Detail & Related papers  (2024-05-08T09:28:04Z)
• Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models [0.8133739801185272]
  The alignments of reasoning abilities between smaller and larger Language Models are largely conducted via Supervised Fine-Tuning (SFT) We propose the Self-refine Instruction-tuning method that elicits Smaller Language Models to self-refine their abilities. Results obtained on commonsense and math reasoning tasks show that this approach significantly outperforms Instruction-tuning in both in-domain and out-domain scenarios.
  arXiv  Detail & Related papers  (2024-05-01T09:10:27Z)
• Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters [65.15700861265432]
  We present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters. To preserve the zero-shot recognition capability of vision-language models, we introduce a Distribution Discriminative Auto-Selector.
  arXiv  Detail & Related papers  (2024-03-18T08:00:23Z)
• Are You Being Tracked? Discover the Power of Zero-Shot Trajectory Tracing with LLMs! [3.844253028598048]
  This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2024-03-10T12:50:35Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• Prompt Highlighter: Interactive Control for Multi-Modal LLMs [50.830448437285355]
  This study targets a critical aspect of multi-modal LLMs' (LLMs&VLMs) inference: explicit controllable text generation. We introduce a novel inference method, Prompt Highlighter, which enables users to highlight specific prompt spans to interactively control the focus during generation. We find that, during inference, guiding the models with highlighted tokens through the attention weights leads to more desired outputs.
  arXiv  Detail & Related papers  (2023-12-07T13:53:29Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)

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.