Generative Modeling of Adversarial Lane-Change Scenario

2025-03-15

AI Summary

To address data scarcity in safety-critical autonomous driving edge cases (e.g., hazardous lane changes) and the difficulty of constructing realistic adversarial scenarios, this paper proposes a data-mining and adversarial-scenario-generation framework for safety-critical decision-making testing. First, high-risk lane-change maneuvers are systematically identified from the NGSIM and INTERACTION datasets. Second, a novel adversarial-scenario-generation mechanism is introduced, jointly optimizing for both sensitivity (to expose model vulnerabilities) and behavioral plausibility (to ensure realism). Third, an environment-aware hybrid model combining Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) is designed to synthesize natural yet highly adversarial vehicle trajectories. Experiments demonstrate that the generated scenarios significantly outperform original datasets and baseline models in collision rate, acceleration volatility, and lane-change aggressiveness—while preserving trajectory naturalness—thereby substantially enhancing the robustness of decision-making models.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Adversarial Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Decision-making in long-tail scenarios is crucial to autonomous driving development, with realistic and challenging simulations playing a pivotal role in testing safety-critical situations. However, the current open-source datasets do not systematically include long-tail distributed scenario data, making acquiring such scenarios a formidable task. To address this problem, a data mining framework is proposed, which performs in-depth analysis on two widely-used datasets, NGSIM and INTERACTION, to pinpoint data with hazardous behavioral traits, aiming to bridge the gap in these overlooked scenarios. The approach utilizes Generative Adversarial Imitation Learning (GAIL) based on an enhanced Proximal Policy Optimization (PPO) model, integrated with the vehicle's environmental analysis, to iteratively refine and represent the newly generated vehicle trajectory. Innovatively, the solution optimizes the generation of adversarial scenario data from the perspectives of sensitivity and reasonable adversarial. It is demonstrated through experiments that, compared to the unfiltered data and baseline models, the approach exhibits more adversarial yet natural behavior regarding collision rate, acceleration, and lane changes, thereby validating its suitability for generating scenario data and providing constructive insights for the development of future scenarios and subsequent decision training.

Problem

• Generates adversarial lane-change scenarios for autonomous driving testing.
• Addresses lack of long-tail scenario data in open-source datasets.
• Uses GAIL and enhanced PPO to refine vehicle trajectory generation.

Innovation

• Generative Adversarial Imitation Learning for scenarios
• Enhanced Proximal Policy Optimization model integration
• Optimized adversarial scenario data generation