Parameterisation of lane-change scenarios from real-world data

Karunakaran, Dhanoop; Berrío, Julie Stephany; Nebot, E.

doi:10.1109/itsc55140.2022.9921981

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…Frameworks have been developed (Karunakaran et al, 2022) to automatically build a dataset of logical lane change scenarios from sensor data, which can be used to sample test concrete scenarios.…”

Section: Prior Workmentioning

confidence: 99%

SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks

Pathrudkar¹,

Venkataraman²,

Kanade³

et al. 2023

Preprint

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One of the major impediments in deployment of Autonomous Driving Systems (ADS) is their safety and reliability. The primary reason for the complexity of testing ADS is that it operates in an open world characterized by its non-deterministic, high-dimensional and non-stationary nature where the actions of other actors in the environment are uncontrollable from the ADS's perspective. This leads to a state space explosion problem and one way of mitigating this problem is by concretizing the scope for the system under test (SUT) by testing for a set of behavioral competencies which an ADS must demonstrate. A popular approach to testing ADS is scenario-based testing where the ADS is presented with driving scenarios from real world (and synthetically generated) data and expected to meet defined safety criteria while navigating through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to enable scenario-based ADS testing. Our work addresses key real-world challenges of building an efficient large scale data ingestion pipeline and search capability to identify scenarios of interest from real world data, creating digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL) testing in ADS simulators and, identifying key scenario parameter distributions to enable optimization of scenario coverage. These along with other modules of SAFR-AV would allow the platform to provide ADS pre-certifications.

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Section: Prior Workmentioning

confidence: 99%

SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks

Pathrudkar¹,

Venkataraman²,

Kanade³

et al. 2023

Preprint

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“…In the initial stage, we propose a novel approach for extracting lane change scenarios from real-world data and represent them in a parameterized form. The set of parameters, previously introduced in our work [ 13 ], characterizes the scenarios. We transform the extracted parameterized collections of lane change interactions into OpenSCENARIO files, thus allowing us to replay the trajectories in a simulator.…”

Section: Edge Case Focused Concrete Scenario Generationmentioning

confidence: 99%

“…To parameterize the real-world lane change scenarios, we used the list of parameters from our previous work [ 13 ]. These parameters came from four control points: scenario start , cut start , cut end , and scenario end .…”

Section: Edge Case Focused Concrete Scenario Generationmentioning

confidence: 99%

“…We transformed raw data into meaningful scenarios through a three-step process. First, we extracted lane change maneuvers from the collected data and converted them into a parameterized representation [ 13 ], thereby validating the parameterization’s effectiveness using metrics to compare the parameterized scenario representation with the original real-world trajectory. Next, we constructed a search space, or parameter space , using extracted parameter values, and we then compared the following three different representations: independent univariate normal, multivariate normal, and a multimodal–multivariate distribution.…”

Section: Introductionmentioning

confidence: 99%

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Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data

Karunakaran,

Berrio Perez,

Worrall

2023

Sensors

Self Cite

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In the past decade, automotive companies have invested significantly in autonomous vehicles (AV), but achieving widespread deployment remains a challenge in part due to the complexities of safety evaluation. Traditional distance-based testing has been shown to be expensive and time-consuming. To address this, experts have proposed scenario-based testing (SBT), which simulates detailed real-world driving scenarios to assess vehicle responses efficiently. This paper introduces a method that builds a parametric representation of a driving scenario using collected driving data. By adopting a data-driven approach, we are then able to generate realistic, concrete scenarios that correspond to high-risk situations. A reinforcement learning technique is used to identify the combination of parameter values that result in the failure of a system under test (SUT). The proposed method generates novel, simulated high-risk scenarios, thereby offering a meaningful and focused assessment of AV systems.

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“…These are then parametrized and modified [6], [7], [19]- [21] to achieve higher variety. Current scenario descriptions are mainly trajectory-based [22]- [26]. An advancement of this is the maneuver-based approach [6], [27], [28], which forms the basis for this paper.…”

Section: Introductionmentioning

confidence: 99%

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Neis,

Beyerer

2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard approach is scenario-based testing, as opposed to distance-based testing primarily performed in field tests. Currently, the time evolution of specific scenarios is mainly described using trajectories, which limit or at least hamper generalizations towards variations. As an alternative, maneuverbased approaches have been proposed. We shed light on the state of the art and available foundations for this new method through a literature review of early and recent works related to maneuver-based scenario description. It includes related modeling approaches originally developed for other applications. Current limitations and research gaps are identified.Index Terms-automated driving, AI, maneuver-based, simulation, scenario-based testing *This publication was written in the context of the AVEAS research project (www.aveas.org), funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program "New Vehicle and System Technologies".

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Parameterisation of lane-change scenarios from real-world data

Cited by 6 publications

References 11 publications

SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks

SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks

Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

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