A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems

Abstract

Scenario modeling for Autonomous Driving Systems (ADS) enables scenario-based simulation and verification which are critical for the development of safe ADS. However, with the increasing complexity and uncertainty of ADS, it becomes increasingly challenging to manually model driving scenarios and conduct verification analysis. To tackle these challenges, we propose a novel and pragmatic framework for scenario modeling, simulation and verification. The novelty is that it’s a verification-in-the-loop scenario modeling framework. The scenario modeling language with formal semantics is proposed based on the domain knowledge of ADS. It facilitates scenario verification to analyze the safety of scenario models. Moreover, the scenario simulation is implemented based on the scenario executor. Compared with existing works, our framework can simplify the description of scenarios in a non-programming, user-friendly manner, model stochastic behavior of vehicles, support safe verification of scenario models with UPPAAL-SMC and generate executable scenario in some open-source simulators such as CARLA. To preliminarily demonstrate the effectiveness and feasibility of our approach, we build a prototype tool and apply our approach in several typical scenarios for ADS.