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2. Suraksha: A Quantitative AV Safety Evaluation Framework to Analyze Safety Implications of Perception Design Choices
 
 # Suraksha: A Quantitative AV Safety Evaluation Framework to Analyze Safety Implications of Perception Design Choices

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 This paper proposes an automated AV safety evaluation framework, Suraksha that quantifies and analyzes the sensitivities of different design parameters on AV safety. It employs a set of driving scenarios generated based on a user-specified difficulty level. It enables the exploration of tradeoffs in requirements either in existing AV implementations to find opportunities for improvement or during the development process to explore the component-level requirements for an optimal and safe AV architecture. As perception is a resource demanding task, we employ Suraksha to analyze the safety effects of using various perception parameters on an industrial AV system.



 ## Authors



Hengyu Zhao (University of California, San Diego)

[Siva Hari](/person/siva-hari)

Timothy Tsai (NVIDIA)

[Michael B. Sullivan](/person/mike-sullivan)

[Steve Keckler](/person/stephen-keckler)

Jishen Zhao (University of California, San Diego)

 

 

 ## Publication Date



Monday, June 21, 2021

 

 ## Published in



[Workshop on Safety and Security of Intelligent Vehicles (SSIV)](https://ieeexplore.ieee.org/document/9502467)

 

 ## Research Area



[Autonomous Vehicles](/research-area/autonomous-vehicles)

[High Performance Computing](/research-area/high-performance-computing)

[Resilience and Safety](/research-area/resilience)

 

 

 ## External Links



[IEEE Digital Library](https://ieeexplore.ieee.org/document/9502467)

 

 

 ## Uploaded Files



[Published manuscript](https://d1qx31qr3h6wln.cloudfront.net/publications/SSIV_2021_Suraksha.pdf "Open file in new window")1.32 MB

 

 

 ## Copyright



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