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Robotics

DrivingAgent: Design and Scheduling Agents for Autonomous Driving Systems

Zhongyu Xia, Yiran Geng, Zixuan Chen, Yujun Li

Featured June 12, 2026

Original Paper

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Simply

A smart system called DrivingAgent uses AI to automatically build new car driving features and then intelligently decides which features to use and when, making self-driving cars faster and safer.

In depth
The paper introduces DrivingAgent, a novel framework that automates the creation and real-time orchestration of modules within complex autonomous driving systems. It features a Design Agent that automatically generates and validates new neural network modules by understanding existing system architectures, and a Scheduling Agent that uses a reinforcement learning-tuned lightweight LLM to dynamically decide when to execute, reuse, or skip these modules based on real-time context and a structured memory bank, thereby optimizing the speed-accuracy trade-off.

Key Takeaways

  • 1
    The framework automates the design and validation of new neural network modules for autonomous driving systems, significantly reducing manual effort.
  • 2
    It employs a dynamic scheduling agent powered by a lightweight, RL-tuned LLM to intelligently orchestrate module execution in real time, adapting to varying contexts.
  • 3
    The approach achieves a superior speed-accuracy trade-off on autonomous driving benchmarks by optimizing computational load through context-aware module invocation and reuse.

Conceptual Flow

HIGH LEVEL
1
Methodology: Automated Design & Dynamic Scheduling

The system first builds new car features automatically, then uses another smart part to decide which features to use right now, like a smart conductor for a car.

Existing Car Features
Car's Needs
Build New Features
New Smart Features
2
Results: Better Speed-Accuracy Balance

By smartly picking which features to use, the car can drive both faster and more accurately than older methods.

Old Driving Systems
DrivingAgent
Compare Performance
DrivingAgent: Faster & Safer