Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs

Savkin, Artem; Ellouze, Rachid; Navab, Nassir; Tombari, Federico

doi:10.1109/iros51168.2021.9636318

Cited by 8 publications

(1 citation statement)

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“…Their main contribution is that they use a scene graph to represent the scenario, which is a hierarchical structure that makes the generation more efficient. In [41], the scene graph is also used to generate image scenarios.…”

Section: Bayesian Networkmentioning

confidence: 99%

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Ding¹,

Xu²,

Arief³

et al. 2022

Preprint

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Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, the large population of cars, in general, leads to an extremely low collision rate, indicating that the safety-critical scenarios are rare in the collected real-world data. Thus, methods to artificially generate scenarios become crucial to measure the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving. We first provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -fidelity, efficiency, diversity, transferability, controllability -and research opportunities lighted up by these challenges.

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Section: Bayesian Networkmentioning

confidence: 99%