Dogun Kim

DriveLM: Driving with Graph Visual Question Answering [ECCV 2024 Oral]https://arxiv.org/abs/2312.14150 DriveLM: Driving with Graph Visual Question AnsweringWe study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human users. While recent approaches adapt VLMs to driving via single..

Asynchronous Large Language Model Enhanced Planner for Autonomous Drivinghttps://arxiv.org/abs/2406.14556 Asynchronous Large Language Model Enhanced Planner for Autonomous DrivingDespite real-time planners exhibiting remarkable performance in autonomous driving, the growing exploration of Large Language Models (LLMs) has opened avenues for enhancing the interpretability and controllability of mo..

LLM+P: Empowering Large Language Modelswith Optimal Planning Proficiencyhttps://arxiv.org/abs/2304.11477 LLM+P: Empowering Large Language Models with Optimal Planning ProficiencyLarge language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LL..

DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experienceshttps://arxiv.org/abs/2406.03008 DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social ExperiencesRecent advancements in foundation models (FMs) have unlocked new prospects in autonomous driving, yet the experimental settings of these studies are preliminary, over-simplified, and ..

DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Modelhttps://arxiv.org/abs/2310.01412 DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language ModelMultimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images ..

LMDrive: Closed-Loop End-to-End Driving with Large Language Modelshttps://arxiv.org/abs/2312.07488 LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsDespite significant recent progress in the field of autonomous driving, modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand, lar..