Map-Agnostic And Interactive Safety-Critical Scenario Generation via Multi-Objective Tree Search

TL;DR

This paper introduces a map-agnostic, multi-objective Monte Carlo Tree Search framework for generating realistic, diverse, and safety-critical traffic scenarios to validate autonomous driving systems, emphasizing explicit interaction logic.

Contribution

It presents a novel multi-objective MCTS approach with hybrid UCB/LCB strategy for interactive, realistic scenario generation across arbitrary maps using SUMO traffic models.

Findings

Achieves 85% collision failure rate in experiments.

Generates scenarios with higher vehicle mileage and CO2 emissions.

Produces more complex and realistic traffic scenarios.

Abstract

Generating safety-critical scenarios is essential for validating the robustness of autonomous driving systems, yet existing methods often struggle to produce collisions that are both realistic and diverse while ensuring explicit interaction logic among traffic participants. This paper presents a novel framework for traffic-flow level safety-critical scenario generation via multi-objective Monte Carlo Tree Search (MCTS). We reframe trajectory feasibility and naturalistic behavior as optimization objectives within a unified evaluation function, enabling the discovery of diverse collision events without compromising realism. A hybrid Upper Confidence Bound (UCB) and Lower Confidence Bound (LCB) search strategy is introduced to balance exploratory efficiency with risk-averse decision-making. Furthermore, our method is map-agnostic and supports interactive scenario generation with each vehicle individually powered by SUMO's microscopic traffic models, enabling realistic agent behaviors in arbitrary geographic locations imported from OpenStreetMap. We validate our approach across four high-risk accident zones in Hong Kong's complex urban environments. Experimental results demonstrate that our framework achieves an 85\% collision failure rate while generating trajectories with superior feasibility and comfort metrics. The resulting scenarios exhibit greater complexity, as evidenced by increased vehicle mileage and CO\(_2\) emissions. Our work provides a principled solution for stress testing autonomous vehicles through the generation of realistic yet infrequent corner cases at traffic-flow level.