Finding Needles in Haystack: Formal Generative Models for Efficient Massive Parallel Simulations
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Abstract (arXiv)
The increase in complexity of autonomous systems is accompanied by a need of data-driven development and validation strategies. Advances in computer graphics and cloud clusters have opened the way to massive parallel high fidelity simulations to qualitatively address the large number of operational scenarios. However, exploration of all possible scenarios is still prohibitively expensive and outcomes of scenarios are generally unknown apriori. To this end, the authors propose a method based on bayesian optimization to efficiently learn generative models on scenarios that would deliver desired outcomes (e.g. collisions) with high probability. The methodology is integrated in an end-to-end framework, which uses the OpenSCENARIO standard to describe scenarios, and deploys highly configurable digital twins of the scenario participants on a Virtual Test Bed cluster.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 3 Jan 2023.
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11 of 32 references.
References · 11
- 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 139
- 2018Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Datacited by 94
- 2018Scenic: A Language for Scenario Specification and Scene Generationcited by 83
- 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 77
- 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
- 2018Scenarios for Development, Test and Validation of Automated Vehiclescited by 48
- 2020Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real Worldcited by 39
- 2017Compositional Falsification of Cyber-Physical Systems with Machine Learning Componentscited by 32
- 2020Waymo's Safety Methodologies and Safety Readiness Determinationscited by 16
- 2021Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Modelscited by 14
- 2018Verifying Controllers Against Adversarial Examples with Bayesian Optimizationcited by 7
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