DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems

Fan, Chuchu; Qi, Bolun; Mitra, Sayan; Viswanathan, Mahesh

doi:10.1007/978-3-319-63387-9_22

Cited by 100 publications

(65 citation statements)

References 55 publications

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“…The initial set of the aircraft was of size 1 in the position components, 0.1 in the speed, and 0.01 in the heading angle. We used Flow* [8] and DryVR [18] to compute reachtubes from scratch for the linear example. We only used DryVR for the aircraft model since our C++ Flow* wrapper does not handle a model having atan2 in the dynamics.…”

Section: Resultsmentioning

confidence: 99%

Multi-agent Safety Verification Using Symmetry Transformations

Sibai

Mokhlesi

Fan

et al. 2020

Tools and Algorithms for the Construction and Analysis of Systems

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We show that symmetry transformations and caching can enable scalable, and possibly unbounded, verification of multi-agent systems. Symmetry transformations map solutions and to other solutions. We show that this property can be used to transform cached reachsets to compute new reachsets, for hybrid and multi-agent models. We develop a notion of virtual system which define symmetry transformations for a broad class of agent models that visit waypoint sequences. Using this notion of virtual system, we present a prototype tool CacheReach that builds a cache of reachtubes for this system, in a way that is agnostic of the representation of the reachsets and the reachability analysis subroutine used.Our experimental evaluation of CacheReach shows up to 66% savings in safety verification computation time on multi-agent systems with 3-dimensional linear and 4-dimensional nonlinear fixed-wing aircraft models following sequences of waypoints. These savings and our theoretical results illustrate the potential benefits of using symmetry-based caching in the safety verification of multi-agent systems.

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Section: Resultsmentioning

confidence: 99%

Multi-agent Safety Verification Using Symmetry Transformations

Sibai

Mokhlesi

Fan

et al. 2020

Tools and Algorithms for the Construction and Analysis of Systems

Self Cite

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“…Formal methods are also used to discover bugs in RVs [30], [31]. However, their models often suffer from state explosion problems, which limits them from porting to complex systems such as RVs.…”

Section: Related Workmentioning

confidence: 99%

PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Kim¹,

Ozmen²,

Bianchi³

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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Unfortunately, previous fuzzing approaches cannot discover this type of violations for the following two reasons. First, they do not consider the entire input space of the RV's control software, including user commands, configuration parameters, and environmental factors. Second, they only focus on finding memory corruption bugs or RV's control stability issues. Therefore, they cannot detect safety policy violations, e.g., a drone is deploying the parachute at a too-low altitude. We develop PGFUZZ, a policy-based fuzzing framework designed to address these challenges. PGFUZZ includes three interconnected components: (1) Pre-Processing, (2) Policy-Guided Fuzzing, and (3) Bug Post-Processing. In the Pre-Processing component, we express the correct operation of an RV through policies denoted by a metric temporal logic (MTL). Thereafter, we minimize the fuzzing space via finding inputs related to the tested policies that, when mutated, could potentially trigger policy violations. For example, given a policy in natural language stating that "the fail-safe mode must be triggered when the engine temperature is higher than 100°C", PGFUZZ expresses this policy with the MTL formula: {(temperature>100°C) → (failsafe=on)}. It then decomposes this formula into the temperature and the fail-safe mode states, and identifies fuzzing inputs such as user commands (e.g., increasing temperature) and configuration parameters (e.g., units of temperature), influencing the policy states. Then, the Policy-Guided Fuzzing mutates inputs identified by the Pre-Processing component. It implements two kinds of distance metrics, propositional distances to guide the mutation engine, and a global distance to detect when a policy violation occurs. The distance metrics quantify how close the current system states are to a policy violation. Positive distances indicate the policy holds, whereas negative distances indicate the policy is violated. Therefore, PGFUZZ mutates inputs to minimize the global distance. After each input is sent to the control software, which runs in an RV simulator, PGFUZZ collects the system states and computes the distance metrics. The input's impact on the distance metric (whether it increases or decreases) is leveraged to decide on the next inputs. When the global distance becomes negative, a policy violation is detected. Turning to the fail-safe mode example, PGFUZZ mutates inputs to increase the temperature to be larger than 100°C, and checks whether, at the same time, the fail-safe mode is activated. The last component, Bug Post-Processing, minimizes the input sequence triggering the bugs by excluding inputs irrelevant to the policy violation. The minimized input sequence is then used to identify the root cause of each violated policy. To verify the correctness and effectiveness of PGFUZZ, we Abstract-Robotic vehicles (RVs) are becoming essential tools of modern systems, including autonomous delivery services, public transportation, and environment monitoring. Despite their diverse deployment, safety and security issues ...

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“…One approach in this direction is discussed in our DryVR framework, which has been used to verify several industrial-scale systems that combine black-and white-box components. 12,13 TLs have been fundamental in understanding the complexity of One challenge for CPS verification is that existing tools-of which there are many strong ones-rely on mathematical models that are disconnected from developer workflows.…”

Section: Giannakopouloumentioning

confidence: 99%

Formal Verification of Cyberphysical Systems

2021

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In Computer, virtual roundtables (VRTs) are virtual panels. We ask a series of questions to a group of experts via email to ascertain the panelists' thoughts about a topic du jour. One difference between VRTs and face-to-face panels is that no expert knows who the others are. That is different from an in-person arrangement, where answers from one participant can affect the responses of others. In this VRT, our topic of discussion is the formal verification (FV) of cyberphysical systems (CPSs). FV is the act of proving the correctness of algorithms with respect to certain formal specifications, using formal methods. Correctness may mean logical definitions of safety, liveliness, and other objectives such as confidentially, integrity, availability, and some version of privacy.

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DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems

Cited by 100 publications

References 55 publications

Multi-agent Safety Verification Using Symmetry Transformations

Multi-agent Safety Verification Using Symmetry Transformations

PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Formal Verification of Cyberphysical Systems

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