Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles

Tuncali, Cumhur Erkan; Pavlic, Theodore P.; Fainekos, Georgios

doi:10.1109/itsc.2016.7795751

Cited by 60 publications

(26 citation statements)

References 17 publications

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“…Only a few techniques have been developed for automatic test case generation of hybrid dynamics addressing the needs of autonomous systems. In [27] proprietary test case generation for automated vehicles was developed based on the S-TaLiRo tool. An approach to infer the vehicle intelligence level from a finite number of tests is presented in [28]; however, this work does not consider fully automatic test case generation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Althoff

Lutz

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

102

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It is apparent that one cannot rely solely on physical test drives for ensuring the correct functionality of autonomous vehicles. Since physical test drives are costly and time consuming, it is advantageous to accompany them with computer simulations. However, since most traffic scenarios are not challenging, even simulations are often too time consuming. To address this issue, we present an approach that creates automatically critical driving situations, i.e., situations with a small solution space for avoiding a collision. Our approach combines reachability analysis for determining the size of the solution space with optimization techniques to shrink it. The solution space is reduced by shifting the initial states of traffic participants, demanding an immediate and correct action of the vehicle under test. We demonstrate our approach by automatically increasing the criticality of several initially uncritical situations recorded from real traffic.

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Section: Introductionmentioning

confidence: 99%

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Althoff

Lutz

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

102

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“…Tuncali et al [139] use formal system requirements, not for formal verification but for falsifying them. They use their automatic test generation tool S-TaLiRo and select simulated annealing for optimization.…”

Section: Simulation-based Falsificationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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“…Similarly, in [16], systems are forced into faulty behavior with respect to previously-defined specifications. In [17] and [18], test case generation for automated vehicles is realized using S-TaLiRo. In [19], an approach using S-TaLiRo is applied to motion planners based on machine learning, including simulated camera processing using deep neural networks.…”

Section: A Related Workmentioning

confidence: 99%

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Klischat

Althoff

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

108

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Virtual testing of automated vehicles using simulations is essential during their development. When it comes to the testing of motion planning algorithms, one is mainly interested in challenging, critical scenarios for which it is hard to find a feasible solution. However, these situations are rare under usual traffic conditions, demanding an automatic generation of critical test scenarios. We present an approach that automatically generates critical scenarios based on a minimization of the solution space of the vehicle under test. By formulating a scenario parametrization and automatic determination of relevant parameter intervals, we are able to optimize the criticality of complex scenarios. We use evolutionary algorithms to tackle the resulting highly nonlinear optimization problem. Compared to our previous approach, we are now able to handle complex situations, in particular those involving intersections. Finally, we demonstrate our approach by generating critical scenarios from initially uncritical scenarios.All authors are with the Technische Universität München,

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Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles

Cited by 60 publications

References 17 publications

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

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