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Publications

Bootstrapped Neural Models for Predicting Self-Driving Vehicle Collisions With Quantified Confidence: Offline and Online Applications

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Accidents in ADScene: a specific survey on AD L2+ functions

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Scenario-based assesment of automated driving systems: How (not) to parameretize scenarios?

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Coverage Metrics for a Scenario Database for the Scenario-Based Assesment of Automated Driving Systems

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Test and validation of perception-based ADAS: modern solutions to traditional challenges

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Workspace Envelope Manipulation for Stewart Platform Simulators with Redundant Degrees of Freedom

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Open-Source Tool Based Framework for Automated Performance Evaluation of an AD Function

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Formulating a dissimilarity metric for comparison of driving scenarios for Automated Driving Systems

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WayWise: A rapid prototyping library for connected, autonomous vehicles

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Bootstrapping Aggregation of Neural Models for Prediction of Intelligent Vehicle Performance with Quantified Uncertainty: Safety Assessment, Failure Analysis, and Online Speed Recommendations

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Automated Vehicle Testing & Data Collection Efforts

Automated Vehicle Testing & Data Collection Efforts

https://doi.org/10.1007/978-3-031-34757-3

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The SUNRISE project is funded by the European Union’s Horizon Europe Research & Innovation Actions under grant agreement No. 101069573.

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Contact Details:

Ainhoa Arrieta
Project Communication
ERTICO
a.arrieta@mail.ertico.com

©2023 Sunrise

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