Quantitative Representation of Autonomous Driving Scenario Difficulty Based on Adversarial Policy Search

Area of Science:

• Autonomous Systems
• Artificial Intelligence
• Machine Learning

Background:

• Autonomous vehicles require continuous performance improvement via learning algorithms to adapt to dynamic traffic.
• The complexity and variability of real-world traffic necessitate methods for representing and generating challenging scenarios for effective AV training.
• Existing methods often rely on discrete, rule-based difficulty representations, limiting adaptability.

Purpose of the Study:

• To propose a data-driven quantitative method for representing scenario difficulty in autonomous driving.
• To enable the generation of targeted, progressively difficult scenarios for robust AV algorithm evolution.
• To develop a system that provides continuous and interpretable difficulty metrics without expert rule design.

Main Methods:

• Introduced the concept of an 'environment agent' trained using reinforcement learning with mechanism knowledge.
• Developed a policy group from environment agent parameters at various training stages to create agents with varying adversarial intensities.
• Utilized these agents to generate data across a spectrum of scenario difficulties within a simulation environment.
• Constructed a data-driven model to quantitatively represent scenario difficulty based on environment agent policies.

Main Results:

• The proposed method effectively generates reasonable, interpretable, and highly discriminative scenarios.
• Quantifiable difficulty representation is achieved without relying on expert logic rules.
• The approach enables continuous, rather than discrete, scenario difficulty representation, outperforming rule-based methods.
• Experimental results validate the effectiveness of the data-driven quantitative representation model.

Conclusions:

• The developed method provides a robust, data-driven approach to quantifying and generating autonomous vehicle training scenarios.
• This facilitates more efficient and targeted evolution of AV algorithms, enhancing their adaptability to complex environments.
• The continuous and interpretable nature of the difficulty representation offers significant advantages over traditional methods.