Probabilistic Validation of Advanced Driver Assistance Systems

Gietelink, OJ; Schutter, Bart De; Verhaegen, Michel

doi:10.3182/20050703-6-cz-1902.02068

Cited by 15 publications

(34 citation statements)

References 8 publications

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“…One solution is to reduce the set Q by neglecting certain subsets that are impossible to occur [6]. Obviously, a low t ttc is more likely with lower values for x r, 0 , v r, 0 , and a 1 , such that the samples for which J = 1 are clustered in a specific subset Q bad .…”

Section: A Principle Of Importance Sampling (Is)mentioning

confidence: 99%

“…However, this measure also requires a priori knowledge of p, which is exactly the parameter we wish to estimate. Furthermore, (6) does not give any information on the confidence interval for any particular estimatep N . In that respect,p N is unlikely to be exactly equal to the real probability p, although it is reasonable to expect thatp N will approach p, as N → ∞, and as long as the samples are chosen to be representative of the set Q.…”

Section: B Monte Carlo Samplingmentioning

confidence: 99%

“…The gain in efficiency can then be calculated by equating (6) and (19). From this follows that in order to get the same level of relative error, the reduction in samples for IS is…”

Section: Sample Complexity Of Ismentioning

confidence: 99%

“…In this paper the controller validation is restricted to the measure of safety, expressed as the probability p that an unsafe situation occurs. In [6] we have used a discrete safety measure ρ s ∈ {0, 1}, where ρ s = 1 means 'collision' and ρ s = 0 means 'no collision'. However, a discrete safety measure cannot distinguish in severity between different situations for which ρ s = 1.…”

Section: A Problem Specificationmentioning

confidence: 99%

“…{b.deschutter,m.verhaegen}@dcsc.tudelft.nl In [6] we have introduced the use of importance sampling (IS) to make Monte Carlo simulation more efficient, i.e. using a much smaller number of tests.…”

mentioning

confidence: 99%

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Adaptive importance sampling for probabilistic validation of advanced driver assistance systems

Gietelink

Schutter

Verhaegen

2006

2006 American Control Conference

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Abstract-We present an approach for validation of advanced driver assistance systems, based on randomized algorithms. The new method consists of an iterative randomized simulation using adaptive importance sampling. The randomized algorithm is more efficient than conventional simulation techniques. The importance sampling pdf is estimated by a kernel density estimate, based on the results from the previous iteration. The concept is illustrated with a simple adaptive cruise control problem.

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Section: A Principle Of Importance Sampling (Is)mentioning

confidence: 99%

Section: B Monte Carlo Samplingmentioning

confidence: 99%

“…The gain in efficiency can then be calculated by equating (6) and (19). From this follows that in order to get the same level of relative error, the reduction in samples for IS is…”

Section: Sample Complexity Of Ismentioning

confidence: 99%

Section: A Problem Specificationmentioning

confidence: 99%

mentioning

confidence: 99%

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Adaptive importance sampling for probabilistic validation of advanced driver assistance systems

Gietelink

Schutter

Verhaegen

2006

2006 American Control Conference

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A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

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Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

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Development of a driver information and warning system with vehicle hardware-in-the-loop simulations

Gietelink¹,

Ploeg²,

Schutter³

et al. 2009

Mechatronics

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a b s t r a c tThis paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VeHIL) simulations the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and more manageable. In the VeHIL laboratory a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and where robot vehicles are used to represent other traffic. In this controlled environment the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VeHIL are demonstrated with test results of a driver information and warning system. Based on the 'V' diagram, the position of VeHIL in the development process of ADASs is illustrated.

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Probabilistic Validation of Advanced Driver Assistance Systems

Cited by 15 publications

References 8 publications

Adaptive importance sampling for probabilistic validation of advanced driver assistance systems

Adaptive importance sampling for probabilistic validation of advanced driver assistance systems

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Development of a driver information and warning system with vehicle hardware-in-the-loop simulations

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