Vadv2: End-to-end vectorized autonomous driving via probabilistic planning
Learning a human-like driving policy from large-scale driving demonstrations is promising,
but the uncertainty and non-deterministic nature of planning make it challenging. In this …
but the uncertainty and non-deterministic nature of planning make it challenging. In this …
Nuscenes-mqa: Integrated evaluation of captions and qa for autonomous driving datasets using markup annotations
Abstract Visual Question Answering (VQA) is one of the most important tasks in autonomous
driving, which requires accurate recognition and complex situation evaluations. However …
driving, which requires accurate recognition and complex situation evaluations. However …
Integrating big data analytics in autonomous driving: An unsupervised hierarchical reinforcement learning approach
In the realm of autonomous vehicular systems, there has been a notable increase in end-to-
end algorithms designed for complete self-navigation. Researchers are increasingly …
end algorithms designed for complete self-navigation. Researchers are increasingly …
LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving
The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding
Vision-language generative AI has demonstrated remarkable promise for empowering cross-
modal scene understanding of autonomous driving and high-definition (HD) map systems …
modal scene understanding of autonomous driving and high-definition (HD) map systems …
Drivecot: Integrating chain-of-thought reasoning with end-to-end driving
End-to-end driving has made significant progress in recent years, demonstrating benefits
such as system simplicity and competitive driving performance under both open-loop and …
such as system simplicity and competitive driving performance under both open-loop and …
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging
Large Language Models (LLMs) with images using a simple projector. Inspired by their …
Large Language Models (LLMs) with images using a simple projector. Inspired by their …
Feedback-Guided Autonomous Driving
J Zhang, Z Huang, A Ray… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
While behavior cloning has recently emerged as a highly successful paradigm for
autonomous driving humans rarely learn to perform complex tasks such as driving via …
autonomous driving humans rarely learn to perform complex tasks such as driving via …
ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles
Abstract We present ChatScene a Large Language Model (LLM)-based agent that
leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous …
leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous …
Large Language Model-based Human-Agent Collaboration for Complex Task Solving
In recent developments within the research community, the integration of Large Language
Models (LLMs) in creating fully autonomous agents has garnered significant interest …
Models (LLMs) in creating fully autonomous agents has garnered significant interest …