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Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with

Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving.

One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.

The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
ContributorsTuncali, Cumhur Erkan (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Kapinski, James (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2019
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Description

System and software verification is a vital component in the development and reliability of cyber-physical systems - especially in critical domains where the margin of error is minimal. In the case of autonomous driving systems (ADS), the vision perception subsystem is a necessity to ensure correct maneuvering of the environment

System and software verification is a vital component in the development and reliability of cyber-physical systems - especially in critical domains where the margin of error is minimal. In the case of autonomous driving systems (ADS), the vision perception subsystem is a necessity to ensure correct maneuvering of the environment and identification of objects. The challenge posed in perception systems involves verifying the accuracy and rigidity of detections. The use of Spatio-Temporal Perception Logic (STPL) enables the user to express requirements for the perception system to verify, validate, and ensure its behavior; however, a drawback to STPL involves its accessibility. It is limited to individuals with an expert or higher-level knowledge of temporal and spatial logics, and the formal-written requirements become quite verbose with more restrictions imposed. In this thesis, I propose a domain-specific language (DSL) catered to Spatio-Temporal Perception Logic to enable non-expert users the ability to capture requirements for perception subsystems while reducing the necessity to have an experienced background in said logic. The domain-specific language for the Spatio-Temporal Perception Logic is built upon the formal language with two abstractions. The main abstraction captures simple programming statements that are translated to a lower-level STPL expression accepted by the testing monitor. The STPL DSL provides a seamless interface to writing formal expressions while maintaining the power and expressiveness of STPL. These translated equivalent expressions are capable of directing a standard for perception systems to ensure the safety and reduce the risks involved in ill-formed detections.

ContributorsAnderson, Jacob (Author) / Fainekos, Georgios (Thesis director) / Yezhou, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements for all autonomous components. Adaptability, on the other hand, involves efficient handling of uncertainty and inconsistencies in models and data. First, I

Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements for all autonomous components. Adaptability, on the other hand, involves efficient handling of uncertainty and inconsistencies in models and data. First, I address safety by presenting a search-based test-case generation framework that can be used in training and testing deep-learning components of AV. Next, to address adaptability, I propose a framework based on multi-valued linear temporal logic syntax and semantics that allows autonomous agents to perform model-checking on systems with uncertainties. The search-based test-case generation framework provides safety assurance guarantees through formalizing and monitoring Responsibility Sensitive Safety (RSS) rules. I use the RSS rules in signal temporal logic as qualification specifications for monitoring and screening the quality of generated test-drive scenarios. Furthermore, to extend the existing temporal-based formal languages’ expressivity, I propose a new spatio-temporal perception logic that enables formalizing qualification specifications for perception systems. All-in-one, my test-generation framework can be used for reasoning about the quality of perception, prediction, and decision-making components in AV. Finally, my efforts resulted in publicly available software. One is an offline monitoring algorithm based on the proposed logic to reason about the quality of perception systems. The other is an optimal planner (model checker) that accepts mission specifications and model descriptions in the form of multi-valued logic and multi-valued sets, respectively. My monitoring framework is distributed with the publicly available S-TaLiRo and Sim-ATAV tools.
ContributorsHekmatnejad, Mohammad (Author) / Fainekos, Georgios (Thesis advisor) / Deshmukh, Jyotirmoy V (Committee member) / Karam, Lina (Committee member) / Pedrielli, Giulia (Committee member) / Shrivastava, Aviral (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021