A Driver’s License Test for Driverless Vehicles

Abbas, Houssam; O’Kelly, Matthew; Rodionova, Alëna; Mangharam, Rahul

doi:10.1115/1.2017-dec-9

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…A combination of simulation-based optimization (Sec-tionVI-D) prior to a reachability analysis is also possible [162], [163]. They call it a robustness-guided verification technique.…”

Section: Reachability Analysismentioning

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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“…A combination of simulation-based optimization (Sec-tionVI-D) prior to a reachability analysis is also possible [162], [163]. They call it a robustness-guided verification technique.…”

Section: Reachability Analysismentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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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)

124

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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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“…11,16] or can be artificially generated with different approaches [e.g. 13,[17][18][19][20]. A central database to store relevant scenarios is proposed in [21].…”

Section: "A Scenario Describes the Temporal Development Between Severmentioning

confidence: 99%

Defining Required and Feasible Test Coverage for Scenario-Based Validation of Highly Automated Vehicles

Amersbach

Winner

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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A statistical, distance-based validation of highly automated vehicles is not feasible due to the high required testing distance. Scenario-based validation approaches promise to solve this issue. However, due to the high number of influence parameters, the number of possible parameter combinations is exploding. Therefore, exhaustive testing of all possible combinations is not feasible as well. Thus, a coverage criterion for scenario-based validation is required. Hereby, it is crucial that all stakeholders accept the coverage criterion. This paper proposes an approach to determine the number of scenarios that correspond to the required testing distance of the known distance-based approach. Furthermore, the number of scenarios that can be feasibly simulated for validation is estimated under certain assumptions. Comparing the required and the feasible number of scenarios shows that there is still a gap of around one order of magnitude. Nevertheless, combining this approach with other methods that aim to reduce the approval effort has the potential to get the required test coverage to a feasible level and therefore contribute to solving the validation challenge. However, there are still many remaining challenges, such as the availability of representative scenario catalogs or sufficient simulation models for environment perception sensors.

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A Driver’s License Test for Driverless Vehicles

Cited by 10 publications

References 13 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Defining Required and Feasible Test Coverage for Scenario-Based Validation of Highly Automated Vehicles

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