Accident Scenario Generation with Recurrent Neural Networks

Jenkins, Ian Rhys; Gee, Ludvig Oliver; Knauss, Alessia; Hang, Y. D.; Schroeder, Jan

doi:10.1109/itsc.2018.8569661

Cited by 39 publications

(30 citation statements)

References 8 publications

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“…By varying the values of the model input parameters, the network can fill a scenario database with many concrete scenarios. Similarly, [61] use Recurrent Neural Networks (RNNs) to model driving data as a sequence and to generate new concrete scenarios from it.…”

Section: A Extraction Of Special Concrete Scenariosmentioning

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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Section: A Extraction Of Special Concrete Scenariosmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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“…LSTM is modeled in a chain structure and can store the information for an extended period. The figure 28 shows the LSTM architecture [140], [145], [146]. The sigmoidal function (σ) takes the output from the last cell and the current input for processing.…”

Section: F Long Short-term Memory (Lstm)mentioning

confidence: 99%

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

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An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

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“…The system uses arrays of test combinations and simulated annealing to find edge scenarios and evolves in time with the feedback from the vehicle's controller. Jenkins et al [23] use recurrent neural networks (RNNs) for the automatic specification of AV test scenarios. The RNNs are applied to existing crash data to create test cases.…”

Section: A Related Workmentioning

confidence: 99%

Autonomous Vehicles Scenario Testing Framework and Model of Computation: On Generation and Coverage

2021

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Autonomous vehicle (AV) technology started to shift the perception of the transportation systems. However, for AVs to operate at their optimum capabilities, they need to go through a comprehensive testing and verification process. While a large amount of research and funding has been provided for solving this problem, there is still a lack of a systematic method to develop standardized tests that can be used to judge if the decision-making capability functions are within acceptable parameters. To that end, the tests need to cover all possible situations that an AV may run into. This paper focuses on defining the notion of coverage mathematically when using pseudo-randomly generated simulations for testing. The approach defines new equivalence relations between scenes, which are the systems' various states, to achieve this goal. Considering the substantial need for computation, even with the obtained coverage, we also introduce the mathematical definition of a sub-scene and additional strategies, such as expanding the equivalence classes of scenes and combining actors in scenes, to reduce the amount of testing required to certify AVs.

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Accident Scenario Generation with Recurrent Neural Networks

Cited by 39 publications

References 8 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Autonomous Vehicles Scenario Testing Framework and Model of Computation: On Generation and Coverage

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