DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
arXiv:2603.18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required for safe driving and make unsafe exploration unavoidable in real-world settings. Recent vision-language models (VLMs) offer promising semantic understanding capabilities; however, their high inference latency and susceptibility to hallucination hinder direct application to real-time vehicle control. To address these limitations, this paper proposes DriveVLM-RL,
The increasing sophistication of Vision-Language Models (VLMs) and the persistent challenges in real-world autonomous driving safety are converging, making a neuroscience-inspired approach timely.
This development represents a significant step towards more reliable and scalable autonomous driving systems by addressing critical limitations of current reinforcement learning and VLM applications.
The integration of neuroscience-inspired reinforcement learning with VLMs could enable autonomous vehicles to achieve a deeper contextual understanding, reducing reliance on expensive and unsafe real-world exploration.
- · Autonomous driving companies
- · AI Safety researchers
- · Logistics and transportation sectors
- · VLM developers
- · Companies relying on traditional RL for AD
- · Human drivers (long term)
- · Simulation-only testing methodologies
Improved safety and efficiency in autonomous vehicles, potentially accelerating their widespread deployment.
Increased investment and research in neuroscience-inspired AI architectures and multimodal models for robotics.
Enhanced AI capabilities could generalize to other complex real-world control problems, such as robotic surgery or industrial automation.
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Read at arXiv cs.AI