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SafeDrive: Knowledge- and data-driven risk-sensitive decision-making for autonomous vehicles with Large Language

Zhiyuan Zhou¹, Heye Huang², Boqi Li³

¹Department of Financial Engineering, University of Southern California, CA 90089, USA.

Accident; Analysis and Prevention

|October 30, 2025

Summary

SafeDrive enhances autonomous vehicle safety using a knowledge- and data-driven framework. It improves decision-making in high-risk scenarios, achieving a 100% safety rate and human-like adaptability.

Keywords:

Autonomous vehicles Knowledge- and data-driven methods Large Language Models Risk-sensitive decision-making

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Area of Science:

Artificial Intelligence
Robotics
Transportation Engineering

Background:

Autonomous vehicles (AVs) excel in normal conditions but struggle with high-risk, long-tail events.
Current AV safety systems require enhancement for dynamic and unpredictable traffic environments.

Purpose of the Study:

To introduce SafeDrive, a novel framework for risk-sensitive decision-making in AVs.
To improve AV safety, adaptability, and performance in complex driving scenarios.

Main Methods:

A modular framework integrating risk quantification, scenario memory, LLM-powered reasoning, and iterative learning.
Utilizing multi-factor risk assessment (driver, vehicle, road interactions).
Employing Large Language Models (LLMs) for context-aware decision-making.

Main Results:

SafeDrive achieved a 100% safety rate in evaluations on real-world datasets (HighD, InD, RounD).
Decision alignment with human-like driving behaviors exceeded 85%.
Demonstrated effective adaptation to unpredictable and high-risk traffic scenarios.

Conclusions:

SafeDrive offers a robust paradigm for integrating knowledge- and data-driven approaches in AVs.
The framework significantly enhances AV safety and adaptability, particularly in long-tail or high-risk situations.
This research paves the way for more reliable and safer autonomous driving systems.