Traffic Scenarios for Automated Vehicle Testing: A Review of Description Languages and Systems (original) (raw)

A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several important problems and compared with each other. This way, scholars and engineers can easily find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

A Driver-Vehicle Model for ADS Scenario-based Testing

2022

Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required micro-simulation, while also supporting behavior reuse and test reproducibility for a wide range of interactive scenarios. To fill this gap between scenario design and execution, we propose the Simulated Driver-Vehicle Model (SDV) to represent and simulate vehicles as dynamic entities with their behavior being constrained by scenario design and goals set by testers. The model combines driver and vehicle as a single entity. It is based on human-like driving and the mechanical limitations of real vehicles for realistic simulation. The layered architecture of the model leverages behavior trees to express high-level behaviors in terms of lower-level maneuvers, affording multiple driving styles and reuse. Further, optimization-based maneuver planner guides the simulated vehicles towards the desired behavior. Our extensive evaluation shows the model's design effectiveness using NHTSA pre-crash scenarios, its motion realism in comparison to naturalistic urban traffic, and its scalability with traffic density. Finally, we show the applicability of SDV model to test a real ADS and to identify crash scenarios, which are impractical to represent using predefined vehicle trajectories. The SDV model instances can be injected into existing simulation environments via co-simulation.

SceML: a graphical modeling framework for scenario-based testing of autonomous vehicles

2020

Ensuring the functional correctness and safety of autonomous vehicles is a major challenge for the automotive industry. However, exhaustive physical test drives are not feasible, as billions of driven kilometers would be required to obtain reliable results. Scenario-based testing is an approach to tackle this problem and reduce necessary test drives by replacing driven kilometers with simulations of relevant or interesting scenarios. These scenarios can be generated or extracted from recorded data with machine learning algorithms or created by experts. In this paper, we propose a novel graphical scenario modeling language. The graphical framework allows experts to create new scenarios or review ones designed by other experts or generated by machine learning algorithms. The scenario description is modeled as a graph and based on behavior trees. It supports different abstraction levels of scenario description during software and test development. Additionally, the graph-based structur...

Collection of Requirements and Model-based Approach for Scenario Description

2021

As the level of automation and variety of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) increases, new challenges for Verification and Validation (V&V) methods emerge. This applies especially in urban areas due to the combination of many different environmental elements, participant types, and interactions between the participants. Scenario-based testing and resimulation of recorded data are promising approaches to tackle these new challenges. An elementary component of these methods is the scenario description, which serves as a connection between different working steps in the V&V workflow. This heterogeneous usage of the scenarios during the development and validation process leads to a multitude of different, sometimes contradictory, demands on the scenario description. Nevertheless, a uniform description is desirable for easy exchange and automation. The contribution of this paper is twofold: Firstly, the described versatile field of demands is systematic...