Title: DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving
Primary area: applications to robotics, autonomy, planning
Keywords: Visual Reasoning, Vision Language Model, Autonomous Driving

Abstract: Effective autonomous driving hinges on robust reasoning across perception, prediction, planning, and behavior. However, conventional end-to-end models fail to generalize in complex scenarios due to the lack of structured reasoning. While recent vision-language models (VLMs) have been applied to driving tasks, they typically rely on isolated modules and static supervision, limiting their ability to support multi-stage decision-making.
  We present AutoDriveRL, a unified training framework that formulates autonomous driving as a structured reasoning process over four core tasks. Each task is independently modeled as a vision-language QA problem and optimized using task-specific reward models, enabling fine-grained reinforcement signals at different reasoning stages. 
  Within this framework, we train DriveRX, a cross-task reasoning VLM designed for multi-stage decision-making. DriveRX achieves strong performance on the public benchmark, outperforming GPT-4o in behavior reasoning and demonstrating robustness under complex or corrupted driving conditions. We further explore the application of VLMs in autonomous driving, including trajectory prediction and action execution. We will release the AutoDriveRL framework and the DriveRX to support future research.