Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

Ponn, Thomas; Müller, Fabian; Diermeyer, Frank

doi:10.1109/ivs.2019.8813794

Cited by 18 publications

(15 citation statements)

References 19 publications

(19 reference statements)

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“…Afterwards they use evolutionary algorithms for optimization by minimizing the safe area and thus maximizing the criticality. Ponn et al [56] built phenomenological sensor models to identify complex test scenarios for the AV's perception capabilities. In addition, some papers do not perform the optimization in advance, but include the AV in the feedback loop.…”

Section: Falsification-based Methodsmentioning

confidence: 99%

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

et al. 2020

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Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests.

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Section: Falsification-based Methodsmentioning

confidence: 99%

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

et al. 2020

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“…[164] can be seen as a starting point for verifying the perception system with barrier certificates. Even in scenario-based testing, there are not many publications that focus particularly on the evaluation of perception [171], [172]. However, the perception is a very important module of AVs [173].…”

Section: ) Functional Decomposition For Scenario Reductionmentioning

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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“…Numerical models of cameras can be used for simulation and digital twin-based testing for automated vehicles. In prior studies [28,34,35], varieties of sensor models with a distinct performance and detail profile were introduced that can replicate the performance of real cameras in simulation. These camera models can be adapted to accommodate specific simulation requirements.…”

Section: Problem Definitionmentioning

confidence: 99%

Phenomenological Modelling of Camera Performance for Road Marking Detection

Tarik

Arefnezhad

et al. 2021

Energies

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With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

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Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

Cited by 18 publications

References 19 publications

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Phenomenological Modelling of Camera Performance for Road Marking Detection

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