Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation

TL;DR

This paper introduces a behavior-guided simulation method for generating risky emergency lane change scenarios in autonomous driving, leveraging a generative adversarial network and reinforcement learning to improve efficiency and realism.

Contribution

It proposes a novel approach combining a GAN-based behavior learning module with a recursive policy optimization for realistic, high-risk scenario generation from limited data.

Findings

Effectively learns emergency lane change behaviors from few samples

Generates high-risk collision scenarios more efficiently than traditional methods

Ensures physical authenticity through model predictive control

Abstract

In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method for generating high risk lane change scenarios. First, a behavior learning module based on an optimized sequence generative adversarial network is developed to learn emergency lane change behaviors from an extracted dataset. This design alleviates the limitations of existing datasets and improves learning from relatively few samples. Then, the opposing vehicle is modeled as an agent, and the road environment together with surrounding vehicles is incorporated into the operating environment. Based on the Recursive Proximal Policy Optimization strategy, the generated trajectories are used to guide the vehicle toward dangerous behaviors for more effective risk scenario exploration. Finally, the reference trajectory is combined with model predictive control as physical constraints to continuously optimize the strategy and ensure physical authenticity. Experimental results show that the proposed method can effectively learn high risk trajectory behaviors from limited data and generate high risk collision scenarios with better efficiency than traditional methods such as grid search and manual design.