Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving

• URL: http://arxiv.org/abs/2409.02914v1
• Date: Wed, 4 Sep 2024 17:52:43 GMT
• Title: Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving
• Authors: Yuhang Lu, Yichen Yao, Jiadong Tu, Jiangnan Shao, Yuexin Ma, Xinge Zhu,
• Abstract summary: We propose IDKB, a large-scale dataset containing over one million data items collected from various countries. Much like the process of obtaining a driver's license, IDKB encompasses nearly all the explicit knowledge needed for driving from theory to practice.
• Score: 24.485164073626674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models. However, LVLMs typically rely on large, general-purpose datasets and lack the specialized expertise required for professional and safe driving. Existing vision-language driving datasets focus primarily on scene understanding and decision-making, without providing explicit guidance on traffic rules and driving skills, which are critical aspects directly related to driving safety. To bridge this gap, we propose IDKB, a large-scale dataset containing over one million data items collected from various countries, including driving handbooks, theory test data, and simulated road test data. Much like the process of obtaining a driver's license, IDKB encompasses nearly all the explicit knowledge needed for driving from theory to practice. In particular, we conducted comprehensive tests on 15 LVLMs using IDKB to assess their reliability in the context of autonomous driving and provided extensive analysis. We also fine-tuned popular models, achieving notable performance improvements, which further validate the significance of our dataset. The project page can be found at: \url{https://4dvlab.github.io/project_page/idkb.html}

Related papers

• Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding [10.242043337117005]
  Vision Large Language Models (VLLMs) have demonstrated impressive capabilities in general visual tasks such as image captioning and visual question answering. However, their effectiveness in specialized, safety-critical domains like autonomous driving remains largely unexplored. We introduce DVBench, a pioneering benchmark designed to evaluate the performance of VLLMs in understanding safety-critical driving videos.
  arXiv  Detail & Related papers  (2025-04-20T07:50:44Z)
• DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding [76.3876070043663]
  We propose DriveLMM-o1, a dataset and benchmark designed to advance step-wise visual reasoning for autonomous driving. Our benchmark features over 18k VQA examples in the training set and more than 4k in the test set, covering diverse questions on perception, prediction, and planning. Our model achieves a +7.49% gain in final answer accuracy, along with a 3.62% improvement in reasoning score over the previous best open-source model.
  arXiv  Detail & Related papers  (2025-03-13T17:59:01Z)
• BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving [0.5735035463793009]
  Large Language Models (LLMs) possess reasoning capabilities and natural language understanding. We introduce BEVDriver, an LLM-based model for end-to-end closed-loop driving in CARLA. On the LangAuto benchmark, our model reaches up to 18.9% higher performance on the Driving Score compared to SoTA methods.
  arXiv  Detail & Related papers  (2025-03-05T00:27:32Z)
• Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives [56.528835143531694]
  We introduce DriveBench, a benchmark dataset designed to evaluate Vision-Language Models (VLMs) Our findings reveal that VLMs often generate plausible responses derived from general knowledge or textual cues rather than true visual grounding. We propose refined evaluation metrics that prioritize robust visual grounding and multi-modal understanding.
  arXiv  Detail & Related papers  (2025-01-07T18:59:55Z)
• AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving [106.0319745724181]
  We introduce AutoTrust, a comprehensive trustworthiness benchmark for large vision-language models in autonomous driving (DriveVLMs) We constructed the largest visual question-answering dataset for investigating trustworthiness issues in driving scenarios. Our evaluations have unveiled previously undiscovered vulnerabilities of DriveVLMs to trustworthiness threats.
  arXiv  Detail & Related papers  (2024-12-19T18:59:33Z)
• DriveMM: All-in-One Large Multimodal Model for Autonomous Driving [63.882827922267666]
  DriveMM is a large multimodal model designed to process diverse data inputs, such as images and multi-view videos, while performing a broad spectrum of autonomous driving tasks. We conduct evaluations on six public benchmarks and undertake zero-shot transfer on an unseen dataset, where DriveMM achieves state-of-the-art performance across all tasks.
  arXiv  Detail & Related papers  (2024-12-10T17:27:32Z)
• CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving [1.727597257312416]
  CoVLA (Comprehensive Vision-Language-Action) dataset comprises real-world driving videos spanning more than 80 hours. This dataset establishes a framework for robust, interpretable, and data-driven autonomous driving systems.
  arXiv  Detail & Related papers  (2024-08-19T09:53:49Z)
• Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving [0.0]
  We develop an efficient, lightweight, multi-frame vision language model which performs Visual Question Answering for autonomous driving. In comparison to previous approaches, EM-VLM4AD requires at least 10 times less memory and floating point operations.
  arXiv  Detail & Related papers  (2024-03-28T21:18:33Z)
• DriveLM: Driving with Graph Visual Question Answering [57.51930417790141]
  We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems. We propose a VLM-based baseline approach (DriveLM-Agent) for jointly performing Graph VQA and end-to-end driving.
  arXiv  Detail & Related papers  (2023-12-21T18:59:12Z)
• 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)
• On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving [37.617793990547625]
  This report provides an exhaustive evaluation of the latest state-of-the-art VLM, GPT-4V. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our findings reveal that GPT-4V demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems.
  arXiv  Detail & Related papers  (2023-11-09T12:58:37Z)
• Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving [6.728693243652425]
  Large Language Models (LLMs) have shown promise in the autonomous driving sector, particularly in generalization and interpretability. We introduce a unique object-level multimodal LLM architecture that merges vectorized numeric modalities with a pre-trained LLM to improve context understanding in driving situations.
  arXiv  Detail & Related papers  (2023-10-03T11:05:14Z)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
  We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
  arXiv  Detail & Related papers  (2021-01-16T23:45:02Z)
• DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis [54.198237164152786]
  Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS) The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development. In this paper, we introduce the Driver Monitoring dataset (DMD), an extensive dataset which includes real and simulated driving scenarios.
  arXiv  Detail & Related papers  (2020-08-27T12:33:54Z)

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.